Patent Publication Number: US-2022215437-A1

Title: Tunable statistical ids

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     The present application is a continuation of U.S. patent application Ser. No. 17/076,552, filed Oct. 21, 2020, entitled “TUNABLE STATISTICAL IDS”, which is a continuation of U.S. patent application Ser. No. 14/791,074, filed Jul. 2, 2015, entitled “TUNABLE STATISTICAL IDS”, issued as U.S. Pat. No. 10,878,457 on Dec. 29, 2020, which claims the benefit of priority to U.S. Provisional Patent Application No. 62/040,185, filed Aug. 21, 2014, entitled “MANAGING TUNABLE PROGRESSIVE STATISTICAL IDS”, all of which are hereby incorporated by reference in their entirety for all purposes. 
     The present application is related to co-pending U.S. patent application Ser. No. 14/791,105, entitled “MANAGING PROGRESSIVE STATISTICAL IDS” (Attorney Docket No. ORA150206-US-NP-2), filed on even date herewith, which is hereby incorporated by reference in its entirety for all purposes. 
    
    
     TECHNICAL FIELD 
     This disclosure relates to the field of managing user identification codes in an internet advertising environment and more particularly to techniques for generating tunable statistical user identification codes. 
     BACKGROUND 
     In order to track user browsing behavior, a website developer puts a unique identifier in a browser cookie to identify a visiting browser. In some cases, use of cookies is not supported (e.g., for some mobile devices), and/or is sometimes disallowed or blocked (e.g., in some versions of Mozilla), or restricted (e.g., to comply with changing privacy standards). Furthermore, with the advent of new rules and regulations (e.g., privacy rules, privacy policies), browser developers have begun to eschew storing 3rd party cookies when a user visits a web page from a first party. Usually a first party web page will include a hidden pixel from a third party website so that the third party can provide additional information to the first party website. Because of the declining frequency of the practice of using third-party cookies, and/or the inability or inconvenience of storing information as a third party cookie, third party websites cannot reliably and uniquely identify a browser using cookie. A third party website needs to use a different (e.g., cookie-less) method to identify a browser or browser-like agent. 
     Unfortunately, many mobile devices and mobile device applications do not support cookies, and indeed, might not support a given particular browser. A different, non-cookie way of identifying a user is needed. One possibility is to use an identifier that exhibits at least a statistical likelihood of corresponding to a user. Unfortunately, a statistical ID (statid or StatID for short) is not necessarily unique to a particular user. This can occur, for example, when multiple browsers from different installations cause generation of the same StatID. In the case that particular StatID is not unique to a user, it cannot be used to unambiguously identify a single unique browser instance or single unique user. An additional challenge of managing StatIDs is trying to generate an identifier as accurate as it can be (e.g., so as to make it more reliable to identify a browser instance or user accurately) yet without generating large numbers of StatIDs that refer to the same user. 
     When mapping user-related information (e.g., a browser header) to a StatID, there can be collisions such as when two sets of user-related information (e.g., profiles) become assigned to the same StatID. Collisions are desired to be minimized (or at least reduced to an acceptable level) since a collision means that two devices might be regarded as the same user (even if this is not true). At the same time fragmentation is to be minimized since, for example, a single browser being used by the same user might be fragmented into two different users, even though actions of the same user might have precipitated the generation of both (fragmented) StatIDs. 
     Techniques are needed to address the problem of how to tune statistical user identification codes with a known degree of accuracy and confidence. None of the aforementioned legacy approaches achieve the capabilities of the herein-disclosed techniques for tuning the generation or mapping functions for statistical user identification codes. Therefore, there is a need for improvements. 
     SUMMARY 
     The present disclosure provides an improved method, system, and computer program product suited to address the aforementioned issues with legacy approaches. More specifically, the present disclosure provides a detailed description of techniques used in methods, systems, and computer program products for tunable progressive statistical user identification codes. The claimed embodiments address the problem of how to generate statistical user identification codes with high quality characteristics. Some claims are directed to approaches for providing a rotating series of individually-tuned hash functions, which claims advance the technical fields for addressing the problem of how to generate statistical user identification codes with high confidence, as well as advancing peripheral technical fields. Some claims improve the functioning of multiple systems within the disclosed environments. 
     One aspect implements a system for generating user identification codes, the system including a database engine to store a plurality of signals comprising characteristics and values received from a user device (e.g., wherein the characteristics and values are based at least in part on a user interaction with the user device); a user ID generator to calculate collision statistics and fragmentation statistics to form a first mapping function that is in turn used to generate a plurality of identification codes based at least in part a first set of selected signals; and a calibration module to produce measurements determined from collision quantities and fragmentation quantities using the first mapping function, wherein the measurements are determined by comparing the plurality of identification codes to entries in a known ID database. 
     Further details of aspects, objectives, and advantages of the disclosure are described below and in the detailed description, drawings, and claims. Both the foregoing general description of the background and the following detailed description are exemplary and explanatory, and are not intended to be limiting as to the scope of the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The drawings, described below, are for illustration purposes only. The drawings are not intended to limit the scope of the present disclosure. The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of fees. 
       FIG.  1 A 1  and FIG.  1 A 2  exemplify environments that are suited for implementation of tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 1B  exemplifies an environment suited for generation of tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 1C  shows a data flow including configuration and generation of tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 1D  shows a possible instance of a feedback loop for generation of tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 2A ,  FIG. 2B  and  FIG. 2C  show possible instances of signal logs used in the generation of tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 3A ,  FIG. 3B  and  FIG. 3C  show collision and fragmentation cases based on signal logs, as used in the generation of tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 4  depicts an ID mapping matrix as used in the generation of tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 5  depicts a feature mapping matrix as used in the generation of tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 6A  is a bridging versus inventory chart as used in the generation of tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 6B  depicts a flow chart as used for evaluating metrics in a system for generating tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 6C  depicts a model performance breakdown chart as used for evaluating metrics in a system for generating tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 7A  is a model tuning breakdown chart as used for evaluating metrics in a system for generating tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 7B  depicts an inventory chart as used for evaluating metrics in a system for generating tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 7C  depicts an inventory chart over selected devices as used for evaluating metrics in a system for generating tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 8A  depicts a feature mapping matrix having an offending feature as used in the generation of tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 8B  depicts a flow chart having a feature selection feedback loop having an offending feature as used in the generation of tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 9A  is a chart depicting a declining value of the utility of a feature through the progression of time, according to some embodiments. 
         FIG. 9B  shows a first-to-last sequence of mapping functions as used in the generation of tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 9C  depicts an updated first-to-last sequence of mapping functions as used in the generation of tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 9D  is a flow chart showing operations and decision as used in the generation of tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 9E  depicts a progression of first-to-last sequences as used in the generation of tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 9F  depicts a system to manage a progression of first-to-last sequences as used in the generation of tunable progressive statistical user identification codes, according to some embodiments. 
         FIG. 10  is a block diagram of a system for tunable progressive statistical user identification codes, according to one embodiment. 
         FIG. 11  is a block diagram of a system for tunable progressive statistical user identification codes, according to one embodiment. 
         FIG. 12A ,  FIG. 12B , and  FIG. 12C  depict exemplary architectures of components suitable for implementing embodiments of the present disclosure, and/or for use in the herein-described environments. 
     
    
    
     DETAILED DESCRIPTION 
     Some embodiments of the present disclosure address the problem of how to generate statistical user identification codes with high confidence and some embodiments are directed to approaches for providing a rotating series of individually-tuned mapping functions. More particularly, disclosed herein and in the accompanying figures are exemplary environments, methods, and systems for tunable progressive statistical user identification codes. 
     Overview 
     Definitions 
     Some of the terms used in this description are defined below for easy reference. The presented terms and their respective definitions are not rigidly restricted to these definitions—a term may be further defined by the term&#39;s use within this disclosure. The term “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the word exemplary is intended to present concepts in a concrete fashion. As used in this application and the appended claims, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless specified otherwise, or is clear from the context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A, X employs B, or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. The articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless specified otherwise or is clear from the context to be directed to a singular form. 
     Reference is now made in detail to certain embodiments. The disclosed embodiments are not intended to be limiting of the claims. 
     Descriptions Of Exemplary Embodiments 
     FIG.  1 A 1  and FIG.  1 A 2  exemplify environment  1 A 100  and environment  1 A 200  respectively that are suited for implementation of tunable progressive statistical user identification codes. 
     As shown in FIG.  1 A 1 , the environment hosts mobile devices  101  (e.g., laptop  102 , an IP phone  103 , a smart phone  104 , and a tablet  105 ) connected to a wireless network  107 , which can serve as a link in a wide area network or local area network (e.g., network  108 ) to which a variety of servers as well as a desktop  109  can be connected. Any of the foregoing can be operated by a user  106 . 
     In the cookie model  121  as shown, the user would browse web pages. A website server  110  serves pages, some of which pages contain a pixel beacon, which can be configured to engage in a protocol  120  with a beacon/pixel server  111 . The beacon/pixel server might be configured to receive a cookie from a user&#39;s machine (e.g., in the case of a desktop). In such a case, the user cookie is sent to a database engine and the characteristics of the user as may be coded into the cookie and stored (e.g., see database engine  112 ). An ad server  113  might recognize that the cookie refers to a user having certain targeted demographics, and the ad server might send an advertisement to be composed onto a web page and presented to the user. Given a statistical ID of the form described herein, an ad server  113  might recognize that the statistical ID refers to a targeted user having certain targeted demographics, and, based on the demographics that derive from the statistical ID, the ad server might send an advertisement to be composed onto a web page and presented to the targeted user. 
     In the StatID model  122 , when the website server  110  serves web pages, the beacon/pixel server might be able to receive only some characteristics from, for example, a browser header. A StatID is generated, and the generated StatID is sent to the database engine to be stored. The beacon/pixel server might request an advertisement based on the StatID and any information that is associated with that StatID. 
     Browser Header (http header) 
     For every browser-based visit to a website, a browser will send a request header called “http header”. This header contains a quantum of information about the browser (e.g., time of visit, browser name and version, device characteristics, IP address, etc.). The beacon/pixel server (or any other server) considers the information from the header as a composition of keywords, called “features” (e.g., IP address, user agent string, etc.). Some keywords are relatively invariant with time (e.g., device type) and some features are more dynamic and/or are rapidly changing with time (e.g., timestamp and IP address). In total, considering all the browsing devices on the Internet, there are a fixed amount of stable keywords. Stable keywords are collected and a dictionary can be created. A selection of features to be used in generation of a StatID (e.g., via a hashing function) is called a “model”. 
     By defining a hash function based on the keywords from the http header, this embodiment generates a  64 -bit hash code to be used as a StatID. The challenges of making it useable includes selecting features (e.g., keywords) from each http header and applying the hash function. The selection of features to use has an impact on various measurable characteristics of a StatID. For example, in the case of using all known features to generate the hash code, every change in an IP or user agent string will generate a different StatID even though it is coming from the same browser, and possibly the same user. This phenomenon is called “fragmentation”. On the other hand, restricting the hashing function to only use a small set of feature keywords may result in compression (e.g., where too many unique profiles are mapped to a single StatID). This phenomenon is termed a “collision”. Exemplary implementations seek to keep both fragmentation and collision low so that a system can identify many browsers reliably while, at the same time, without generating too many fragmented StatIDs for a single browser. 
     As can be appreciated, fragmentation and collisions are two competing criteria. In one aspect, a process analyzes empirical data so as to choose a set of keywords that has the right amount of fragmentation and collision. In one implementation a process assigns each keyword a unique code (e.g., a ucode) and maintains such an assignment (e.g., in a ucode dictionary). Once a ucode is assigned to a keyword the assignment does not vary over time. The value of ucodes are to provide input to the hash function to generate the final hash code as a StatID. A collection of all keywords encountered becomes a master dictionary or master model. Some embodiments select keywords from a master model to form a keyword subset as a functional model based on the empirical data and deterministic criteria. Keywords that do not appear in the functional model will not be used in the hash code generation, only those keywords in the functional model will be used to generate the final hash code, i.e., statistical ID. 
     Whenever a new keyword is observed from the Internet (e.g., from log files), the master model is updated by adding the new keywords and ucodes while keeping the existing keywords and ucodes unchanged. Therefore, whenever a new functional model is created, the same code for the same keyword is maintained. As long as a browser sends the http header with the recognized keywords in the functional model, the same StatID will be generated. This way the same StatID for the browser through model update or software upgrade can be maintained. One case where the new functional model generates a different StatID for the same browser occurs when the new functional model contains new keywords (or in the case that keywords have been removed). This technique is referred to as “progressive” StatID generation. The progressive property supports tuning StatID performance continuously to both cater for changes in the business climate while maintaining a continuous tracking ability of the StatIDs. The aspect of creating a functional model from a master model such that the accuracy of StatID generation can be controlled over a progression of time is further discussed in the following paragraph and in other places below. 
     One embodiment operates as follows: For every update or upgrade, the process will determine the differences in keywords between the ‘old’ functional model and the ‘new’ functional model. This set of differences is called a “difference set”. Only those browsers that have the keywords belonging to the difference set will have a different generated StatID in the new model, as comparing to the old model. However, if a browser has no keywords in the difference set, their StatID will remain the same. In order to reduce the impact of changing many StatIDs in this embodiment, a carefully constructed functional model is generated such that only a small fraction of browsers will be affected. This concept is used in progressive StatID generation techniques. 
     Another embodiment tunes the accuracy of the StatID based on choosing a trade-off point between fragmentation and collision for each device or geo-location. Since most keywords are distinct for each device and its IP range can indicate the geographical location (country level), The StatID performance can be independently controlled by generating different functional models for different combinations of devices and geo-location. This concept is used in tunable StatID generation techniques. 
     In yet another aspect, a systematic procedure to tune the performance of the StatID is disclosed. In a theoretical case, if every website can interact with every browser on the Internet, then theoretically, there exists one universal optimal statistical ID. However, in the empirical cases, the browser data encountered by each website are different as different businesses attract different users; therefore, there is no one single most optimal statistical ID for all cases. 
     What is desired is to have the ability to tune the performance of a statistical ID such that one business can tune its statistical ID according to one&#39;s unique business tradeoffs. The disclosed techniques and processes facilitate an operator to tune its statistical ID generation by trading off collision and fragmentation to achieve a total number of statistically measurable and unique StatID. 
     One possible environment for managing tradeoffs when generating StatIDs is shown in  FIG. 1B . 
       FIG. 1B  exemplifies an environment  1 B 00  suited for generation of tunable progressive statistical user identification codes, which can be used in any context, including uses to extend or replace uses of user IDs in the disclosed systems. As used herein, a user ID can be any unique identifier that can be used to access aspects of that user. Strictly as examples, a user ID can be a name or an email alias, or a hashed email alias, or a device ID that corresponds to a device used by a user, or a pointer, or an identifier that is formed from a collection of attributes ascribed to a particular user. A user ID can be generated by the user, or can be provided by a third-party or can be formed using any one or more algorithms. A user ID can refer uniquely to a particular individual. In some situations, a user ID can refer to a set of users that share one or more user attributes (e.g., demographics or interests). 
     As shown in  FIG. 1B , a user  106  updates the user&#39;s mobile device (e.g., buys a new device or advances to a new software load). The website server serves pages, which in turn will encounter a beacon, and new non-cookie information is sent to the beacon/pixel server. A new StatID is generated. The specific nature of the new StatID and its generation are the subject of the following figures and descriptions. 
       FIG. 1C  shows a data flow  1 C 00  including configuration and generation of tunable progressive statistical user identification codes. As an option, one or more instances of data flow  1 C 00  or any aspect thereof may be implemented in the context of the architecture and functionality of the embodiments described herein. Also, the data flow  1 C 00  or any aspect thereof may be implemented in any desired environment. 
     As shown in  FIG. 1C , the data flow comprises an input set  148  that serves as inputs to a statistical ID generator  140 . Inputs include non-cookie information (e.g., INFO1, INFO2, INFO3 . . . INFO9 thru INFOn) and a configuration module (e.g., configuration module  146 ). Shown outputs include a StatID. 
     The statistical ID generator (e.g., a user id generator) includes a collision statistics module  142  and a fragmentation statistics module  144 ). An operator can provide a set of non-cookie information and a set of configuration parameters, and analyze collision statistics and fragmentation statistics in order to change the inputs and/or configuration parameters. In some cases a calibration module is provided, and an operator can change the inputs and/or configuration parameters based on statistics from the calibration module. 
       FIG. 1D  shows a possible instance of a feedback system  1 D 00  for generation of tunable progressive statistical user identification codes. As an option, one or more instances of feedback system  1 D 00  or any aspect thereof may be implemented in the context of the architecture and functionality of the embodiments described herein. Also, the feedback system  1 D 00  or any aspect thereof may be implemented in any desired environment. 
     As shown in  FIG. 1D , the feedback loop  160  comprises a loop back from an output of the calibration module  150  to a configuration module (e.g., configuration module  146 ). The calibration module takes in inputs in the form of generated StatIDs (e.g., from StatID database  156 ) and a set of database entries corresponding to known profiles (e.g., from known ID database  158 ). The calibration module can calculate true collision statistics from the true collision statistics module  152  as well as calculate true fragmentation statistics from the true fragmentation statistics module  154  so as to calibrate the configuration and/or other aspects of the statistical ID generation module. A statistical ID generation module, or any other module, can calculate collision statistics that indicate how many characteristics, or what set of characteristics are expected to be associated with different users. For example, one statistical identification code generator can use a set of characteristics to generate a code that would be undistinguished from a large number of known users (e.g., exhibiting a high likelihood of collisions), while a different statistical identification code generator can use a different set of characteristics to generate a code that would be distinguished from nearly all known users (e.g., exhibiting a low likelihood of collisions). 
     Further details regarding general approaches to user profiles are described in U.S. application Ser. No. 62/040,197, titled “A MULTI-TIER REGIME FOR CREATING AND MANAGING ONLINE USER PROFILES” filed on Aug. 21, 2014, which is hereby incorporated by reference in its entirety. 
     Further details regarding general approaches to generation of user identification codes are described in U.S. application Ser. No. 13/918,091 titled “MULTI-PROFILE TRACKING IDENTIFICATION OF A MOBILE USER” filed on Jun. 14, 2013, which is hereby incorporated by reference in its entirety. 
     The non-cookie information comprises browser headers and other data collected by the beacon/pixel server. 
       FIG. 2A  through  FIG. 2C  shows possible instances of signal logs  2 A 00 , signal logs  2 B 00  and signal logs  2 C 00  used in the generation of tunable progressive statistical user identification codes. As an option, one or more instances of signal logs  2 A 00 , signal logs  2 B 00  and signal logs  2 C 00  or any aspect thereof may be implemented in the context of the architecture and functionality of the embodiments described herein. Also, the signal logs  2 A 00 , signal logs  2 B 00  and signal logs  2 C 00  or any aspect thereof may be implemented in any desired environment. 
     As shown in  FIG. 2A ,  FIG. 2B  and  FIG. 2C , the signal logs comprise an Internet Protocol (IP) address (e.g., IP address), a user-agent string (UA) and other HTTP information. Some of the information in the http header is human-readable, and some of it is encoded. Either species (e.g., human readable information and/or encoded information) can be logged. Either species can be included in the ucode dictionary. The examples shown and discussed as pertaining to  FIG. 2A  and  FIG. 2B  are merely subsets of signals. Such subsets can be relatively smaller or relatively larger, for example, and as shown in  FIG. 2C . The signals can comprise a time indication, a time zone indication, a plug-in indication, and a MIME TYPE indication, and/or any other signal indications and/or signal values. Any signal indications and/or signal values can originate from a user&#39;s platform (e.g., mobile device, software version, browser, etc.) or network. 
       FIG. 3A ,  FIG. 3B  and  FIG. 3C  show collision and fragmentation cases based on signal logs, as used in the generation of tunable progressive statistical user identification codes. As an option, one or more instances of collision and fragmentation cases based on the signal logs  3 A 00 , signal logs  3 B 00 , and signal logs  3 C 00  or any aspect thereof may be implemented in the context of the architecture and functionality of the embodiments described herein. Also, the collision and fragmentation cases based on the signal logs  3 A 00 , signal logs  3 B 00 , and signal logs  3 C 00  or any aspect thereof may be implemented in any desired environment. 
     As shown in  FIG. 3A  through  FIG. 3C , the collision and fragmentation cases based on the signal logs of  FIG. 2A  through  FIG. 2C  comprises three examples. First, a newly-encountered DEVICE-ID (e.g., A1  302 ) is mapped to a new StatID (e.g., B1  304 ). Second, a collision operation based upon an incoming pair of DEVICE-ID (e.g., A2  306  and A3  310 ) is mapped to the same StatID (e.g., B2  308 ). Third, a fragmentation operation based on one incoming X_UIDH (e.g., A4  312 ) is mapped to three StatIDs (e.g., B2  308 , B3  314 , and B4  316 ). 
       FIG. 4  depicts an ID mapping matrix  400  as used in the generation of tunable progressive statistical user identification codes. As an option, one or more instances of ID mapping matrix  400  or any aspect thereof may be implemented in the context of the architecture and functionality of the embodiments described herein. Also, the ID mapping matrix  400  or any aspect thereof may be implemented in any desired environment. 
     As shown in  FIG. 4 , the ID mapping matrix comprises rows of DEVICE-ID values (e.g., A1, A2, A3, A4, as shown), and columns of StatIDs (e.g., B1, B2, B3, and B4, as shown). The value in a cell of the matrix refers to a mapping. As shown, A2 and A3 collide since they both map to B2  402  (also see collision statistic  405 ). Also as shown, A4 is fragmented since it is mapped to both B3 and B4  406  (also see fragmentation statistic  404 ). 
     A metric (e.g., in equation form) for collisions and fragmentations are given by: 
     
       
         
           
             
               
                 
                   Collsion 
                   = 
                   
                     
                       N 
                       ⁡ 
                       
                         ( 
                         
                           c 
                           &gt; 
                           1 
                         
                         ) 
                       
                     
                     
                       
                         N 
                         ( 
                         
                           c 
                           = 
                         
                       
                       ⁢ 
                       *) 
                     
                   
                 
               
               
                 
                   ( 
                   
                     Eq 
                     . 
                     
                         
                     
                     ⁢ 
                     1 
                   
                   ) 
                 
               
             
             
               
                 
                   Fragmentation 
                   = 
                   
                     
                       N 
                       ⁡ 
                       
                         ( 
                         
                           f 
                           &gt; 
                           1 
                         
                         ) 
                       
                     
                     
                       
                         N 
                         ( 
                         
                           f 
                           = 
                         
                       
                       ⁢ 
                       *) 
                     
                   
                 
               
               
                 
                   ( 
                   
                     Eq 
                     . 
                     
                         
                     
                     ⁢ 
                     2 
                   
                   ) 
                 
               
             
           
         
       
     
       FIG. 5  depicts a feature mapping matrix  500  as used in the generation of tunable progressive statistical user identification codes. As an option, one or more instances of feature mapping matrix  500  or any aspect thereof may be implemented in the context of the architecture and functionality of the embodiments described herein. Also, the feature mapping matrix  500  or any aspect thereof may be implemented in any desired environment. 
     As shown in  FIG. 5 , the feature mapping matrix comprises a set of features (e.g., “Mozilla”, “iPhone”, “iOS”, “Android”, “OS”, and “US”). The StatIDs shown at the left (e.g., S1, S2, S3, and S4) are values comprised of the existence or absence of a particular feature. 
     In some cases the aforementioned value can be constructed by a concatenation of features. In some cases, the feature of an IP address or portion thereof serves to discriminate between two StatIDs that would otherwise collide: 
       Word={User Agent, IP}  (Eq. 3)
 
     The use of an IP address or portion thereof has several implications (e.g., an indication or geography), some of which are discussed in the following  FIG. 6A . 
       FIG. 6A  is a bridging versus inventory chart  6 A 00  as used in the generation of tunable progressive statistical user identification codes. As an option, one or more instances of bridging versus inventory chart  6 A 00  or any aspect thereof may be implemented in the context of the architecture and functionality of the embodiments described herein. Also, the bridging versus inventory chart  6 A 00  or any aspect thereof may be implemented in any desired environment. 
     As shown in  FIG. 6A , the bridging versus inventory chart shows several possible implementations of a bridging versus inventory metric. As shown, the bridging versus inventory chart  6 A 00  includes implementations of three variants of EQ. 3: 
       Word 3Octet ={User Agent, 3 octets of IP address}  (Eq. 4)
 
       Word 3.5Octet ={User Agent, 3.5 octets of IP address}  (Eq. 5)
 
       Word 4Octet ={User Agent, 4 octets of IP address}  (Eq. 6)
 
     The implementation of EQ. 4, EQ. 5, and EQ. 6 produce several compositions of the Word, namely composition1  606 , composition2  608 , and composition3  610 . Additionally, and as shown, composition4  612  is formed from just the user agent information. Construction and uses of these compositions can be based on a geography  604  and/or characteristics of user devices (e.g., user characteristics of user agent  602 ). Characteristics and any respective values can be based on the user device (e.g., which device and which OS, etc.), and/or the user agent (e.g., which browser or browser version, or application, or app is in use, etc.), and/or any use model (e.g., what time periods the device is detected to be in use, etc.) and/or any characteristic that can be detected based on user interaction with the device. Further examples are given in the following  FIG. 6B . 
       FIG. 6B  depicts a flow chart  6 B 00  as used for evaluating metrics in a system for generating tunable progressive statistical user identification codes. As an option, one or more instances of flow chart  6 B 00  or any aspect thereof may be implemented in the context of the architecture and functionality of the embodiments described herein. Also, the flow chart  6 B 00  or any aspect thereof may be implemented in any desired environment. 
     As shown in  FIG. 6B , the flow chart comprises several processes. The flow commences by using user agent information to generate a StatID (see process  620 ). The generated StatIDs are stored in a user agent StatID database  622 . Next, one or more of the aforementioned constructions can be formed by adding a portion of the IP address as input to a statistical ID generator (see process  624 ). The constructions are stored in a database of StatIDs with IP addresses (see construction database  626 ). Next, as shown, a process serves to evaluate qualities of the composed StatIDs (see process  628 ), and the qualities are organized into a report (see operation  630 ). Strictly as one example, the model performance breakdown chart of  FIG. 6C  gives one form of such a report. 
       FIG. 6C  depicts a model performance breakdown chart  6 C 00  as used for evaluating metrics in a system for generating tunable progressive statistical user identification codes. As an option, one or more instances of model performance breakdown chart  6 C 00  or any aspect thereof may be implemented in the context of the architecture and functionality of the embodiments described herein. Also, the model performance breakdown chart  6 C 00  or any aspect thereof may be implemented in any desired environment. 
       FIG. 6C , depicts a model performance breakdown chart comprising a series of performance metrics pertaining to a mapping function to generate StatIDs. A column “Nx” refers to a calibrated number of known unique profiles (e.g., known unique users  660 ), and a column “Ny” gives the number of generated StatIDs  662  for the shown breakdown. Model performance can be measured as pertains to the qualities of a mapping function. Statistical metrics can be calculated over a particular mapping function. For example, statistical metrics can be used to estimate the extent of users distinguished based on a given set of characteristics. When there is a database of known users that are known to be associated with a particular characteristic or set of characteristics is available, then a particular mapping function can be generated (e.g., based on some portion of the same set of characteristics) so as to model or estimate correspondence to matching users (e.g., inventory) in the database. In some embodiments, a particular mapping function is used to determine a set of users that are distinguished based on a set of characteristics. The determined set of users can be measured to determine coverage over a population of IDs for which a similar or identical set of characteristics is known. When a high degree of coverage of the known population is measured, then it can be statistically predicted (e.g., within a confidence interval) how much coverage could be expected given a larger population. 
     The techniques to determine coverage of a population (e.g., inventory of users) can be used to determine a correlation to input signals (e.g., input signals from the aforementioned signal logs). For example, when a mapping function based on a set of input signals is deemed to provide a statistically measurable degree of coverage over a known population of IDs, then it follows that the same input signals used in the mapping function would be present (e.g., at least to the extent of a calculable confidence interval) in a larger population of generated IDs, such as would be generated over time from operation of user devices. 
     The aforementioned known population can be used to evaluate various qualities of a mapping function. Two of such qualities, namely fragmentation and collision, are depicted in  FIG. 6C . In particular, columns “F” and “C” and “avgF” and “avgC” provide measures for the quality of the generated StatIDs (e.g., see fragmentation quality  664  and collision/compression quality  666 ). Any one or more quantities, including any one or more of the shown performance metrics can be normalized (e.g., to a value between 0 and 1) and/or any combination of two or more of the shown performance metrics can be normalized. 
     The normalized performance metrics can be used in implementing model tuning techniques, some of which are shown and described in the following figures. 
       FIG. 7A  is a model tuning breakdown chart  7 A 00  as used for evaluating metrics in a system for generating tunable progressive statistical user identification codes. As an option, one or more instances of model tuning breakdown chart  7 A 00  or any aspect thereof may be implemented in the context of the architecture and functionality of the embodiments described herein. Also, the model tuning breakdown chart  7 A 00  or any aspect thereof may be implemented in any desired environment. 
     As shown in  FIG. 7A , the model tuning breakdown chart compares the rise in inventory as a normalized tuning parameter is increased. As shown, there is a point of diminishing returns, beyond which point inventory coverage increases very slowly (see trend  702  and trend  704 ). 
     The origin (0,0) of this model tuning breakdown chart corresponds to the case where all sets of browser signals or user agent (UA) signals map through a statistical ID mapping function to the same statistical ID; there is no fragmentation. As features are added to the mapping function inputs (e.g., iPhone=TRUE), then the generated statistical ID takes on a greater range of possible values, which can be used to discriminate between one user profile and another user profile. Adding additional features would continuously produce more statistical IDs, however there is a reachable limit where adding more input to the mapping function would produce more statistical ID values even though the additional statistical IDs do not map to any additional profiles. For example, adding the characteristic “Born after 1800=TRUE” would not map to any additional profiles since all profiles would already carry this value. As shown in trend  702  and trend  704  there is a point in the trend where an incremental rise in inventory is smaller than a corresponding incremental increase in the tuning parameter. In many cases, that point can be selected as a desired level of fragmentation (e.g., a point of diminishing returns). In other cases, a still higher (or lower) degree of fragmentation is selected, so as to meet a given inventory requirement or constraint. Selecting a higher degree of fragmentation often means accepting the risk that two different statistical IDs actually represent the same person. Selecting a higher degree of bridging often means accepting the risk that one particular statistical IDs actually represents multiple different persons. Points of diminishing returns are shown and discussed as pertaining to  FIG. 7B . 
       FIG. 7B  depicts an inventory chart  7 B 00  as used for evaluating metrics in a system for generating tunable progressive statistical user identification codes. As an option, one or more instances of bridging versus inventory chart  7 B 00  or any aspect thereof may be implemented in the context of the architecture and functionality of the embodiments described herein. Also, the bridging versus inventory chart  7 B 00  or any aspect thereof may be implemented in any desired environment. 
     As shown in  FIG. 7B , the inventory chart annotates a point of diminishing returns. Inventory quantities can be measured and any one or more of various methods can be used to determine a point of diminishing returns (e.g., a lower point of inflection, a higher point of inflection). An inventory and quantities thereto can be based on any characteristics or measures of the features, and/or combination of features and portions of the IP address. An inventory based on geography (e.g., USA or Germany) and a mobile device operating system type (e.g., Android, iOS) is given in the following  FIG. 7C . 
     In some cases a statistical ID mapping function is tuned based one or more parameters that are endemic to a particular geography or commercial marketplace. Such a case is shown in the example of  FIG. 7A , which is further annotated in  FIG. 7B  to show inventory in a “Germany” marketplace as compared with a “US” marketplace. A lower point of inflection might be determined for one given marketplace and a higher point of inflection might be determined for another given marketplace. A marketplace can be further divided into sub-markets that correspond to user agent features. Strictly as one example,  FIG. 7C  depicts the case where a selected agent feature can include a device type or device platform (e.g., iOS platforms versus Android platforms). 
     Some embodiments calculate collision statistics fragmentation statistics, and inventory statistics contemporaneously, so as to form a tuned mapping function. Collision statistics, fragmentation statistics, inventory levels, confidence interval statistics, and other quantities can be calculated using a known ID database  158 . In some situations, collision statistics are dominant (e.g., so as to avoid an overly inclusive set of signals), and in other situations fragmentation statistics are dominant (e.g., so as to avoid generating multiple IDs for the same user). In still other situations, inventory levels are dominant. For example, after generation of a candidate mapping function, the candidate mapping function can be used to generate identification codes (e.g., StatIDs) based on signals present in a database of known IDs, and then comparing the set of generated identification codes to ID entries in a set of known IDs to determine projected inventory quantities or ratios. The projected inventory quantities are based at least in part on a number of known IDs that are mapped to by the candidate mapping function. If the candidate mapping function maps to, for example, 70% of the users in a sample set (e.g., database of known IDs), then it can be predicted (e.g., within a calculable confidence interval) that the same candidate mapping function would map to, for example, 70% of the new users (e.g., users without IDs in a sample set. The accuracy (e.g., confidence interval) of the prediction can be calculated—the larger the sample set, the more accurate the prediction will be. 
     The signals used to form a candidate mapping function can be selected with respect to a particular sample set. For example, a signal involving a portion of an IP address that is tied to a particular geographic location (e.g., Germany) can be considered, and the sample set might be selected to include only users that identify as “German”, or “in Germany”. Many or fewer such signals can be selected (e.g., based on a device type, or based on an operating system, etc.). Many variations are possible, some of which variations are shown and discussed as pertaining to  FIG. 7C . 
       FIG. 7C  depicts an inventory chart over selected devices  7 C 00  as used for evaluating metrics in a system for generating tunable progressive statistical user identification codes. As an option, one or more instances of fragmentation versus inventory chart over selected devices  7 C 00  or any aspect thereof may be implemented in the context of the architecture and functionality of the embodiments described herein. Also, the fragmentation versus inventory chart over selected devices  7 C 00  or any aspect thereof may be implemented in any desired environment. 
     As shown in  FIG. 7C , the inventory chart over selected device platforms comprises a comparison of inventory with respect to fragmentation based on device operating system. The depiction of  FIG. 7C  is purely exemplary, and myriad other possibilities exist and can be configured (e.g., via a query made to a database engine). In addition to the aforementioned inventories and trends (e.g., trend  702  and trend  704 ), inventory curves are shown for US-based Android devices (e.g., see trend  708 ) and Germany-based Android devices (e.g., see trend  710 ). 
     Inventory curves and trends (and any points of diminishing returns) can be presented in chart form such as given in  FIG. 7C . Such curves can be calculated and shown for any feature or geography or any combination of features and/or geographies. 
       FIG. 8A  depicts a feature mapping matrix  8 A 00  having an offending feature as used in the generation of tunable progressive statistical user identification codes. As an option, one or more instances of feature mapping matrix  8 A 00  or any aspect thereof may be implemented in the context of the architecture and functionality of the embodiments described herein. Also, the feature mapping matrix  8 A 00  or any aspect thereof may be used for device tuning, and/or may be implemented in any desired environment. 
     As shown in  FIG. 8A , the example feature mapping matrix  806  comprises StatIDs S1 through S4, each of which are mapped to a device characteristic. In this example, device characteristics pertaining the iOS map  803  are “Mozilla”, “iPhone”, and “iOS”. The device characteristics pertaining to the Android map  805  are “Android”, “Linux”, and “Mobile”. 
     By observation, and as shown, the “Mobile” mapping of StatID S3 contains an offending feature. The offending feature causes undesirable effects. The depiction of  FIG. 8A  illustrates fragmentation for two devices: Device 1 (S1, S2), and Device 2 (S3, S4). The features “Linux” and “Mobile” causes the SID fragmentation of Device 1 and “Mobile” causes fragmentation on Device 2. The feature “Mobile” causes two devices to fragment and feature “Linux” causes only one device to fragment. 
       FIG. 8B  depicts a flow chart having a feature selection feedback loop  8 B 00  having an offending feature as used in the generation of tunable progressive statistical user identification codes. As an option, one or more instances of flow chart having a feature selection feedback loop  8 B 00  or any aspect thereof may be implemented in the context of the architecture and functionality of the embodiments described herein. Also, the flow chart having a feature selection feedback loop  8 B 00  or any aspect thereof may be implemented in any desired environment. 
     As shown in  FIG. 8B , the flow chart having a feature selection feedback loop comprises a feature selector module to identify candidate features (see module  802 ), and a module to apply an objective function to the selection of candidate features (see operation  804 ). The application of an objective function can involve a large number of combinations of features. 
     Progressive Statistical IDs 
       FIG. 9A  is a chart  9 A 00  depicting a declining value of the utility of a feature through the progression of time. Certain features may have high utility at the time they first appear, but may decline in value or utility over time. Strictly as one example, the iOS version (e.g., iOS version 5.3) might have had high utility to discriminate between different users. However, as time progresses, additional iOS versions are released (e.g., “IOSv6”, “IOSv7”, “IOSv8”, etc.), and the older versions fade into disuse. Over a certain passage of time, few users use “IOS version 5.3” so it is not useful in any mapping function, and can be dropped (see operation  958  of  FIG. 9D ). The aforementioned iOS version is merely one illustrative example. Many features that were at one moment in time useful in the context of a mapping function to produce a StatID mapping function might become less useful or obsolete as time passes. 
     Many of the StatID mapping functions heretofore discussed are optimized for features, signals and user observations assessed at a particular point in time. Yet, over the passage of time, the quality of the Stat ID model tends to decline as new features emerge, and as popular devices and/or their operating systems, and/or app usage patterns change, and as user behavior changes. Strictly as examples, the introduction of new phones, operating systems, apps and browser versions can introduce new features and/or signals and/or any formatting of keywords into user agent strings. Such new features and/or signals and/or any formatting of keywords would not have been supported in earlier mapping functions. Such new features and/or signals and/or keywords can affect the measurements and/or calibration of a mapping function. Strictly as an example, certain operating systems may be observed to have an initially-high adoption rates (e.g., at the moment when a previous mapping function was calibrated), but later, that same operating system might be observed have a much have lower adoption rate. In a contrary example, newly-introduced mobile devices can have low adoption upon initial introduction, and then later, exhibit a higher adoption rate. Such changes in the frequency of observations of features and/or user behavior may introduce unwanted levels of fragmentation or unwanted levels of collisions. Some optimizations can be approached by considering a combination of factors in an optimization function that accounts for a total number of duplicates as well as an overall coverage of the second set. Some optimizations can be approached by defining an optimization function that considers one or more factors that are inversely correlated with the overall coverage (e.g., for a certain range of totals). Some optimization functions solve for a maximization (or minimization) of one variable subject to one or more constraints of other variables. 
     One way to manage such a changing landscape of features is to recalibrate a new mapping function periodically to form a progression of mapping functions, so as to optimize the selected feature set to account for more recently-observed signals. Such recalibration serves to maintain high performance with respect to accuracy, fragmentation, collisions, and any other quality metrics. A progression, specifically as first-to-last sequence is depicted in  FIG. 9B . 
       FIG. 9B  shows a first-to-last sequence  9 B 00  of mapping functions as used in the generation of tunable progressive statistical user identification codes. As an option, one or more instances of first-to-last sequence  9 B 00  or any aspect thereof may be implemented in the context of the architecture and functionality of the embodiments described herein. Also, the first-to-last sequence  9 B 00  or any aspect thereof may be implemented in any desired environment. 
     Adding New Features to a Progression of Statistical ID Mapping Functions 
     As shown in  FIG. 9B , the first-to-last sequence comprises a progression from mapping function F0 (e.g., using one set of features) through to mapping function F5 (e.g., using a different set of features). Each step in the progression corresponds to one or more new features being brought into a corresponding mapping function. For example, a new version of iOS may come to market, and can be used in the calculation of a StatID for a user. It is possible to progressively provide new mapping functions as new features become available. The first-to-last sequence  9 B 00  is formed by progressively adding a mapping function to the regime. As depicted, the model initially comprises only mapping function FO that becomes activated at time=T 0 . At time T 1 , mapping function F1 becomes activated, and mapping function F0 remains activated. At time T 1 , mapping function F2 becomes activated, and mapping functions F0 and F1 remain activated, and so on. 
     Eliminating Features from a Progression of Statistical ID Mapping Functions 
     Similarly, some features fade into disuse and are not prevalent enough to influence quality metrics. Deprecated features should be progressively eliminated, while bringing in new features that are emerging and/or becoming prevalent. To do so, a series of hashing functions can be formed into a progression sequence (e.g., oldest to newest) and as new features are included in a StatID generation process, old features are deprecated and eventually eliminated. 
     Such a regime for adding a newly-generated hashing function to a sequence and obsoleting the oldest hashing function is shown and described as pertaining to  FIG. 9C . 
       FIG. 9C  depicts an updated first-to-last sequence  9 C 00  of mapping functions as used in the generation of tunable progressive statistical user identification codes. As an option, one or more instances of updated first-to-last sequence  9 C 00  or any aspect thereof may be implemented in the context of the architecture and functionality of the embodiments described herein. Also, the updated first-to-last sequence  9 C 00  or any aspect thereof may be implemented in any desired environment. 
     As shown in  FIG. 9C , a rotation is shown by juxtaposing an older model progression next to a newer model progression. The new model progression adds the new hashing function F6 (see addition  908 ) and renders obsolete the oldest hashing function F0 (see obsolete  906 ). This is merely one example, and longer (or shorter) sequences are possible. As is further discussed below, a model progression can be changed very frequently. A new mapping function in a model progression sequence can be added asynchronously with the deprecation of a mapping function in the same model progression sequence. 
     A new mapping function can add features that will aid in differentiating users based on recent activity. At the same time, users or profiles that do not possess the new feature need not change their respective StatIDs. Avoiding changing the mapping function(s) that map to profiles that do not possess the new feature serves to maintain continuity for data providers that have stored data against these IDs. It also serves the needs of data buyers that have purchased data against these IDs. This situation presents two apparently-conflicting objectives. That is, a new mapping function using new features should be added as frequently as the utility of new features are discovered. However, adding a new mapping function should not cause churn or otherwise negatively impact continuity for data providers that have stored data against these IDs and/or for data buyers that have purchased data. 
     One possibility for managing the aforementioned apparently-conflicting objectives is to add features aggressively to add a new mapping function to a progression. This has the advantage of reducing collisions, however it also means that the results of the new mapping function need to be stored—consuming storage space. Another possibility for managing the aforementioned apparently-conflicting objectives is to aggressively remove or render obsolete mapping functions from a progression—releasing storage space. Each of these options have additional desirable and undesirable effects as is depicted in the following table. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Desirable and Undesirable Effects 
               
            
           
           
               
               
               
            
               
                   
                 Aggressively Add  
                 Aggressively Eliminate  
               
               
                   
                 Mapping Functions 
                 Mapping Functions 
               
               
                   
               
               
                 Pro/Desirable 
                 Better user profile  
                 Releases storage space 
               
               
                   
                 discrimination when  
                   
               
               
                   
                 using the new 
                   
               
               
                   
                 mapping functions 
                   
               
               
                 Con/Undesirable 
                 Consumes storage  
                 Data purchasers cannot  
               
               
                   
                 space 
                 retrieve profiles using  
               
               
                   
                   
                 older mapping functions 
               
               
                   
               
            
           
         
       
     
     One way to optimize for adding new features and maintaining stable ID access is to keep all features from the previous model and add any new features identified by a recalibration process into a new mapping function. For example, features “Mozilla”, “Safari”, and “10_9)3” have existed as part of an older model (e.g., “model v1”). A recalibration process determines features of a newer model (e.g., “model v2”) to use features “Safari” and “10_9_4”. The “model v2” would include the following features “Mozilla”, “Safari”, “10_9_3” and “10_9_4”. 
     Another way to optimize for adding new features and maintaining stable ID access is to evaluate the features in previous model that are not part of the current model and remove the features as possible that result in a retrieval impact that is less than a threshold number or percentage of users. The threshold number or percentage of users can be based on an acceptable level of lost or churned IDs. Following the foregoing example, the two features in “model v2” that are not in “model v1” are “Mozilla”, and “10_9_3”. If “Mozilla” continues to be present in (for example) 7% of user agent strings, but “10_9_3” is present in only 0.01% of user agent strings. In such a case a threshold percentage value of 5% would exclude “10_9_3” while keeping “Mozilla”. The times at which to invoke a recalibration process can follow a particular periodicity, or, a time at which to invoke a recalibration process can be triggered whenever collision statistics and/or fragmentation statistics are calculated. 
       FIG. 9D  is a flow chart  9 D 00  showing operations and decision as used in the generation of tunable progressive statistical user identification codes. The shown flow commences at START and selects a set of new features and adds them to a previous mapping function to form a candidate mapping function (see operation  951 ). The metrics for the mapping function based on the selected features are quantified, (see operation  952 ) and if the metric are OK (e.g., acceptable levels of fragmentation, acceptable levels of collisions) then the flow proceeds to evaluate the impact of dropping old features (see operation  955 ). If the metrics are not OK (see decision  956 ) then loop  973  is taken and a different selection of the features is considered (see operation  953 ). 
     When the different selection of features is considered (see operation  953 ) the impact of a particular feature is compared against a threshold amount. If the impact of dropping that particular feature is below a threshold, then the particular feature can be dropped. After such consideration (e.g., over the entire set of old features) then the flow will assign a new mapping function, and will use the remaining features in the new mapping function (see operation  958 ). 
     The flow can be performed at any moment in time, and the flow might result in a decision to add a new mapping function (see decision  984 ). It is also possible to execute the flow, or a portion of the flow (e.g., deprecation flow  959 ) so as to make a decision to deprecate an older mapping function (see decision  980 ). Decisions to add a new mapping function and/or decisions to deprecate an older mapping function can be made independently. A first-to-last sequence can be increased in length by decision to add a new mapping function. Or, a first-to-last sequence can be decreased in length by decision to deprecate an older mapping function. Decisions that result in an increased length of a first-to-last sequence or decisions that results in a decreased length of first-to-last sequence can be taken independently. In some cases a decisions to add a new mapping function can be made contemporaneously with a decision to keep (e.g., not deprecate) an older mapping function (see decision  982 ) when forming a new first-to-last sequence. 
       FIG. 9E  is a chart  9 E 00  depicting a progression of first-to-last sequences as used in the generation of tunable progressive statistical user identification codes, according to some embodiments. 
       FIG. 9F  is a chart  9 F 00  depicting a system to manage a progression of first-to-last sequences as used in the generation of tunable progressive statistical user identification codes. The shown system comprises operational units for managing a progression of user identification code generators. As shown, the system includes a statistical ID generator  140  that serves to generate mapping functions from respective selections of signals. The mapping functions are stored in a StatID database  156 . The StatID database can store a plurality of mapping functions as progression sequences, and such progression sequences can be accessed by a progression management module  960  (e.g., see the IO port). The plurality of mapping functions corresponds to a sequence of mapping functions. A sequence has a first mapping function and a last mapping function, and any instance of a mapping function from the sequence of mapping functions can generate user identification codes. The statistical ID generator further comprises logic for constructing an incremental mapping function, which incremental mapping function uses at least some inputs different from the other mapping functions of the progression sequence. A deprecation module  962  serves to mark one or more mapping function of the sequence of mapping functions as deprecated, and a sequencing module  964  serves to establish the incremental mapping function as a new mapping function within the sequence of mapping functions. A progression storage module  966  can store the progression into the StatID database. In some cases a configuration can be set to establish a new sequence of mapping functions to be used beginning at a particular moment in time. 
     Additional Embodiments of the Disclosure 
     Tunable Statistical ID 
       FIG. 10  is a block diagram of a system for tunable progressive statistical user identification codes, according to some embodiments. The system includes a processor module  1010  and commences upon constructing a first hash function, the hash function using inputs comprising at least some features selected from user agent information (see module  1020 ), then modifying the first hash function to define a second hash function to accept a combination of features plus an IP address (see module  1030 ), measuring the collision and fragmentation quantities using the second hash function over a set of browser signals (see module  1040 ), measuring the inventory quantities using the second hash function over the set of browser signals (see module  1050 ); and determining a different set of selected features to define a third hash function (see module  1060 ). 
     Steps for generating user identification codes can have many commencement point and/or variations. In one embodiment, such steps commence upon receiving a first plurality of signals (e.g., generated from operation of user devices), where at least some of the first plurality of signals comprise characteristics of respective user devices. Such characteristics can derive from user interactions with respective user devices. A mapping function generator calculates the extent of collisions and fragmentation statistics when a generated mapping function is applied over a population of IDs (e.g., a test set) for which the characteristics are known. For example, collision statistics predict how many different users are assigned to the same identification codes. When the population of IDs is covered to a particular degree (e.g., collisions are sufficiently low) then the generated mapping function can be used to generate a plurality of identification codes that derive from new incoming signals (e.g., from the signal logs), and the coverage of the new, incoming population can be predicted at least within a statistical confidence interval. More particularly, a mapping function and a first set of IDs (for which user correspondence is known) can be used to estimate the extent of coverage of an arbitrary or random second set of users that are distinguished based on the same characteristics. A coverage prediction value (and confidence interval) can be determined by comparing measured coverage of the first set (and the size of the first set) to determine how many IDs of the second set of IDs would be covered by identification codes generated from the first mapping function. When the prediction value (and confidence interval) for an arbitrary or random second set of users is deemed to be covered to a particular degree by using the generated mapping function, then the mapping function can be used to approximate IDs based on an arbitrary (e.g., future) set of incoming signals as would be generated over time from operation of the user devices. 
     In some cases, determining that the IDs of the second set are covered to a particular degree excludes duplicates, or permits coverage over duplicates only to a threshold amount of duplicates (e.g., so as to not introduce bias into the collision or fragmentation statistics). In some cases determining that the IDs of the second set are covered to a particular degree comprises reaching or surpassing a threshold amount, which threshold amount can be based on a curve such as an inventory curve. In some cases an optimization function can be defined that maximizes one factor or variable subject to one or more constraints of other factors or variables. 
     Progressive Statistical ID 
       FIG. 11  is a block diagram of a system for tunable progressive statistical user identification codes, according to some embodiments. The system includes a processor module  1110  and commences upon receiving a plurality of mapping functions, the plurality of hash functions corresponding to a first-to-last sequence of mapping functions (see module  1120 ), then constructing an incremental hash function, the hash function using at least some inputs (e.g., features) different from the inputs to the hash function of the last sequence (see module  1130 ), marking the first hash function of the a first-to-last sequence as deprecated (see module  1140 ); and deploying the incremental hash function as the new hash function of a new first-to-last sequence (see module  1150 ). 
     System Architecture Overview 
     Additional System Architecture Examples 
       FIG. 12A  depicts a block diagram of an instance of a computer system  12 A 00  suitable for implementing embodiments of the present disclosure. Computer system  12 A 00  includes a bus  1206  or other communication mechanism for communicating information, which interconnects subsystems and devices such as a processor  1207 , a system memory (e.g., main memory  1208 , or an area of random access memory RAM), a static storage device (e.g., ROM  1209 ), an internal or external storage device  1210  (e.g., magnetic or optical), a data interface  1233 , a communication interface  1214  (e.g., PHY, MAC, Ethernet interface, modem, etc.), a display  1211  (e.g., CRT or LCD), input devices  1212  (e.g., keyboard, cursor control), and an external data repository  1231 . 
     According to an embodiment of the disclosure, computer system  12 A 00  performs specific operations by processor  1207  executing one or more sequences of one or more instructions contained in system memory. Such instructions may be read into system memory from another computer readable/usable medium such as a static storage device or a disk drive. The sequences can be organized to be accessed by one or more processing entities configured to execute a single process or configured to execute multiple concurrent processes to perform work. A processing entity can be hardware-based (e.g., involving one or more cores) or software-based or can be formed using a combination of hardware and software that implements logic and/or can carry out computations and/or processing steps using one or more processes and/or one or more tasks and/or one or more threads or any combination therefrom. 
     According to an embodiment of the disclosure, computer system  12 A 00  performs specific networking operations using one or more instances of communication interface  1214 . Instances of the communication interface  1214  may comprise one or more networking ports that are configurable (e.g., pertaining to speed, protocol, physical layer characteristics, media access characteristics, etc.) and any particular instance of the communication interface  1214  or port thereto can be configured differently from any other particular instance. Portions of a communication protocol can be carried out in whole or in part by any instance of the communication interface  1214 , and data (e.g., packets, data structures, bit fields, etc.) can be positioned in storage locations within communication interface  1214 , or within system memory, and such data can be accessed (e.g., using random access addressing, or using direct memory access (DMA), etc.) by devices such as processor  1207 . 
     The communications link  1215  can be configured to transmit (e.g., send, receive, signal, etc.) communications packets  1238  comprising any organization of data items. The data items can comprise a payload data area  1237 , a destination address  1236  (e.g., a destination IP address), a source address  1235  (e.g., a source IP address), and can include various encodings or formatting of bit fields to populate the shown packet characteristics  1234 . In some cases the packet characteristics include a version identifier, a packet or payload length, a traffic class, a flow label, etc. In some cases the payload data area  1237  comprises a data structure that is encoded and/or formatted to fit into byte or word boundaries of the packet. 
     In some embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement aspects of the disclosure. Thus, embodiments of the disclosure are not limited to any specific combination of hardware circuitry and/or software. In some embodiments, the term “logic” shall mean any combination of software or hardware that is used to implement all or part of the disclosure. 
     The term “computer readable medium” or “computer usable medium” as used herein refers to any medium that participates in providing instructions to processor  1207  for execution. Such a medium may take many forms including, but not limited to, non-volatile media and volatile media. Non-volatile media includes, for example, optical or magnetic disks such as disk drives or tape drives. Volatile media includes dynamic memory such as a random access memory. 
     Common forms of computer readable media includes, for example, floppy disk, flexible disk, hard disk, magnetic tape, or any other magnetic medium; CD-ROM or any other optical medium; punch cards, paper tape, or any other physical medium with patterns of holes; RAM, PROM, EPROM, FLASH-EPROM, or any other memory chip or cartridge, or any other non-transitory medium from which a computer can read data. Such data can be stored, for example, in any form of external data repository  1231 , which in turn can be formatted into any one or more storage areas and which can comprise parameterized storage  1239  accessible by a key (e.g., filename, table name, block address, offset address, etc.). 
     In an embodiment of the disclosure, execution of the sequences of instructions to practice the disclosure is performed by a single instance of the computer system  12 A 00 . According to certain embodiments of the disclosure, two or more instances of computer system  12 A 00  coupled by a communications link  1215  (e.g., LAN, PTSN, or wireless network) may perform the sequence of instructions required to practice embodiments of the disclosure using two or more instances of components of computer system  12 A 00 . 
     The computer system  12 A 00  may transmit and receive messages, data, and instructions including programs (e.g., application code), through communications link  1215  and communication interface  1214 . Received program code may be executed by processor  1207  as it is received and/or stored in the shown storage device or in or upon any other non-volatile storage for later execution. Computer system  12 A 00  may communicate through a data interface  1233  to a database  1232  on an external data repository  1231 . Data items in a database can be accessed using a primary key (e.g., a relational database primary key). A module as used herein can be implemented using any mix of any portions of the system memory and any extent of hard-wired circuitry including hard-wired circuitry embodied as a processor  1207 . Some embodiments include one or more special-purpose hardware components (e.g., power control, logic, sensors, transducers, etc.). A module may include one or more state machines and/or combinational logic used to implement or facilitate managing tunable progressive statistical IDs. 
     Various implementations of the database  1232  comprise storage media organized to hold a series of records or files such that individual records or files are accessed using a name or key (e.g., a primary key or a combination of keys and/or query clauses). Such files or records can be organized into one or more data structures (e.g., data structures used to implement or facilitate aspects of managing tunable progressive statistical IDs). Such files or records can be brought into and/or stored in volatile or non-volatile memory. 
       FIG. 12B  depicts a block diagram  12 B 00  of an instance of a client device  12 B 01  that may be included in a system implementing instances of the herein-disclosed embodiments. Client device  12 B 01  may include many more or fewer components than those shown in  FIG. 12B . Client device  12 B 01  may represent, for example, an embodiment of at least one of client devices as heretofore disclosed. 
     As shown in the figure, client device  12 B 01  includes a client device processor  1240  in communication with a client device memory  1242  via a client device memory bus  1241 . Client device  12 B 01  also includes a power supply  1251 , one or more client device network interfaces  1254 , an audio interface  1255 , a client device display  1256 , a client device keypad  1257 , an illuminator  1258 , a video interface  1259 , a client device IO interface  1260 , a haptic interface  1261 , and a GPS transceiver  1253  for global positioning services. 
     The power supply  1251  provides power to client device  12 B 01 . A rechargeable or non-rechargeable battery may be used to provide power. The power may also be provided by an external power source such as an AC adapter or a powered docking cradle that supplements and/or recharges a battery. 
     A client device  12 B 01  may optionally communicate with a base station, or directly with another computing device. A client device network interface  1254  includes circuitry for coupling a client device  12 B 01  to one or more networks, and is constructed for use with one or more communication protocols and technologies including, but not limited to, global system for mobile communication (GSM), code division multiple access (CDMA), time division multiple access (TDMA), user datagram protocol (UDP), transmission control protocol/Internet protocol (TCP/IP), short message service (SMS), general packet radio service (GPRS), wireless access protocol (WAP), ultra wide band (UWB), IEEE 802.16 Worldwide Interoperability for Microwave Access (WiMax), session initiated protocol/real-time transport protocol (SIP/RTP), or any of a variety of other wireless communication protocols. Client device network interface  1254  is sometimes known as a transceiver, a transceiving device, or a network interface card (NIC). 
     An audio interface  1255  is arranged to produce and receive audio signals such as the sound of a human voice. For example, audio interface  1255  may be coupled to a speaker and microphone to enable telecommunication with others and/or generate an audio acknowledgement for some action. 
     A client device display  1256  may be a liquid crystal display (LCD), gas plasma, light emitting diode (LED), or any other type of display used with a computing device. A client device display  1256  may also include a touch sensitive screen arranged to receive input from an object such as a stylus or a digit from a human hand. 
     A client device keypad  1257  may comprise any input device arranged to receive input from a user. For example, client device keypad  1257  may include a push button numeric dial, or a keyboard. A client device keypad  1257  may also include command buttons that are associated with selecting and sending images. 
     An illuminator  1258  may provide a status indication and/or provide light. Illuminator  1258  may remain active for specific periods of time or in response to events. For example, when the illuminator  1258  is active, it may backlight the buttons on client device keypad  1257  and stay on while the client device is powered. Also, the illuminator  1258  may backlight these buttons in various patterns when particular actions are performed such as dialing another client device. An illuminator  1258  may also cause light sources positioned within a transparent or translucent case of the client device to illuminate in response to actions. 
     A video interface  1259  is arranged to capture video images such as a still photo, a video segment, an infrared video or the like. For example, the video interface  1259  may be coupled to a digital video camera, a web-camera or the like. A video interface  1259  may comprise a lens, an image sensor, and other electronics. Image sensors may include a complementary metal-oxide-semiconductor (CMOS) integrated circuit, charge-coupled device (CCD), or any other integrated circuit for sensing light. 
     Some instances of the shown client device  12 B 01  comprise a client device IO interface  1260  for communicating with external devices such as a headset, or other input or output devices not shown in  FIG. 12B . The client device IO interface  1260  can use one or more communication technologies such as a USB, infrared, Bluetooth™ port or the like. A haptic interface  1261  is arranged to as a human interface device (HID) to facilitate interaction with a user of a client device. Such interaction can include tactile feedback to a user of the client device. For example, the haptic interface  1261  may be employed to cause vibration of the client device  12 B 01  in a particular way (e.g., with a pattern or periodicity) and/or when interacting with one or another user. 
     A GPS transceiver  1253  can determine the physical coordinates of client device  12 B 01  on the surface of the Earth. The GPS transceiver  1253 , in some embodiments, may be optional. The shown GPS transceiver  1253  outputs a location such as a latitude value and a longitude value. However, the GPS transceiver  1253  can also employ other geo-positioning mechanisms including, but not limited to, triangulation, assisted GPS (AGPS), enhanced observed time difference (E-OTD), cell identifier (CI), service area identifier (SAI), enhanced timing advance (ETA), base station subsystem (BSS) or the like, to determine the physical location of client device  12 B 01  on the surface of the Earth. It is understood that under different conditions, a GPS transceiver  1253  can determine a physical location within millimeters for client device  12 B 01 ; and in other cases, the determined physical location may be less precise such as within a meter or significantly greater distances. In certain embodiments, the client device  12 B 01  may provide other information that may be employed to determine a physical location of the device including, for example, a media access control (MAC) address, IP address, IP port identifier, or the like. 
     The client device memory  1242  includes random access memory  1243 , read-only memory  1249 , and other storage means. The client device memory  1242  illustrates an example of computer readable storage media (devices) for storage of information such as computer readable instructions, data structures, program modules or other data. The client device memory  1242  stores a basic IO system (BIOS) in the embodiment of client device BIOS  1250  for controlling low-level operation of client device  12 B 01 . The memory also stores an operating system  1244  for controlling the operation of client device  12 B 01 . It will be appreciated that this component may include a general-purpose operating system such as a version of UNIX, or LINUX™, or a specialized client communication operating system such as Microsoft Corporation&#39;s Windows Mobile™, Apple Corporation&#39;s iOS™, Google Corporation&#39;s Android™ or the Symbian® operating system. The operating system may include, or interface with a Java virtual machine module that enables control of hardware components and/or operating system operations via Java application programs. 
     The client device memory  1242  further includes one or more instances of client device data storage  1245 , which can be used by client device  12 B 01  to store, among other things, client device applications  1246  and/or other data. For example, client device data storage  1245  may also be employed to store information that describes various capabilities of client device  12 B 01 . The information may then be provided to another device based on any of a variety of events including being sent as part of a header during a communication, sent upon request or the like. Client device data storage  1245  may also be employed to store social networking information including address books, buddy lists, aliases, user profile information or the like. Further, client device data storage  1245  may also store messages, web page content, or any of a variety of content (e.g., received content, user generated content, etc.). 
     At least a portion of the information may also be stored on any component or network device including, but not limited, to a client device processor&#39;s readable storage media  1252 , a disk drive or other computer readable storage devices within client device  12 B 01 , etc. 
     An instance of a client device processor&#39;s readable storage media  1252  may include volatile, nonvolatile, removable, and non-removable media implemented in any method or technology for storage of information such as computer- or processor-readable instructions, data structures, program modules, or other data. Examples of computer readable storage media include RAM, ROM, electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, Compact disc read-only memory (CD-ROM), digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other physical medium which can be used to store the desired information and which can be accessed by a computing device. The aforementioned readable storage media  1252  may also be referred to herein as computer readable storage media. 
     The client device applications  1246  may include computer executable instructions which, when executed by client device  12 B 01 , transmit, receive, and/or otherwise process network data. The network data may include, but is not limited to, messages (e.g., SMS, multimedia message service (MMS), instant message (IM), email, and/or other messages), audio, video, and enable telecommunication with another user of another client device. Client device applications  1246  may include, for example, a messenger  1262 , a browser  1247 , and any instances of other applications  1248 . Certain other applications  1248  may include, but are not limited to, calendars, search programs, email clients, IM applications, SMS applications, voice over Internet protocol (VOIP) applications, contact managers, task managers, transcoders, database programs, word processing programs, security applications, spreadsheet programs, games, search programs, and so forth. In some embodiments, other applications  1248  may collect and store user data that may be received from other computing devices in the environment. 
     A messenger  1262  may be configured to manage a messaging session using any of a variety of messaging communications including, but not limited to email, SMS, IM, MMS, internet relay chat (IRC), Microsoft IRC (mIRC), really simple syndication (RSS) feeds, and/or the like. For example, in certain embodiments, the messenger  1262  may be configured as an IM application such as AOL (America Online) instant messenger, Yahoo! messenger, .NET messenger server, (ICQ) or the like. In certain embodiments, the messenger  1262  may be configured to include a mail user agent (MUA) such as Elm, Pine, message handling (MH), Outlook, Eudora, Mac Mail, Mozilla Thunderbird or the like. In another embodiment, the messenger  1262  may be a client device application that is configured to integrate and employ a variety of messaging protocols including, but not limited, to various push and/or pull mechanisms for client device  12 B 01 . In certain embodiments, the messenger  1262  may interact with the browser  1247  for managing messages. As used herein, the term “message” refers to any of a variety of messaging formats, or communications form including, but not limited to, email, SMS, IM, MMS, IRC or the like. 
     A browser  1247  may include virtually any application configured to receive and display graphics, text, multimedia, messages and the like, employing virtually any web based language. In certain embodiments, the browser application is enabled to employ HDML, WML, WMLScript, JavaScript, SGML, HTML, XML and the like, to display and send a message. However, any of a variety of other web-based programming languages may be employed. In certain embodiments, a browser  1247  may enable a user of client device  12 B 01  to communicate with another network device as may be present in the environment. 
       FIG. 12C  depicts a block diagram  12 C 00  of an instance of a network device  12 C 01  that may be included in a system implementing instances of the herein-disclosed embodiments. Network device  12 C 01  may include many more or fewer components than those shown. Network device  12 C 01  may be configured to operate as a server, client, peer, a host, or any other device. 
     The network device  12 C 01  includes at least one instance of a network device processor  1270 , instances of readable storage media  1283 , network interface(s)  1287 , a network device IO interface  1285 , a hard disk drive  1286 , a video display adapter  1284 , and a network device memory  1271 , all in communication with each other via a network device memory bus  1290 . The network device memory generally includes network device RAM  1272 , network device ROM  1281 . Some embodiments include one or more non-volatile mass storage devices such as a hard disk drive  1286 , a tape drive, an optical drive, and/or a floppy disk drive. The network device memory stores a network device operating system  1273  for controlling the operation of network device  12 C 01 . Any general-purpose operating system may be employed. A basic input/output system (BIOS) is also provided in the form of network device BIOS  1282  for controlling the low-level operation of network device  12 C 01 . As illustrated in  FIG. 12C , a network device  12 C 01  also can communicate with the Internet, or some other communications network, via a network interface unit  1287 , which is constructed for use with various communication protocols including the TCP/IP protocol. The network interface unit  1287  is sometimes known as a transceiver, a transceiving device, or a network interface card (NIC). Network device  12 C 01  also comprises a network device IO interface  1285  for communicating with external devices such as a keyboard or other input or output devices. A network device IO interface  1285  can use one or more communication technologies such as USB, infrared, Bluetooth™ or the like. 
     The storage devices as described above may use various types of computer readable media, namely non-volatile computer readable storage media and/or a client device processor&#39;s readable storage media  1283  and/or a network device processor&#39;s readable storage media  1283 . Such media may include any combinations of volatile, nonvolatile, removable, and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules, or other data. Examples of processor readable storage media include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other media which can be used to store the desired information and which can be accessed by a computing device. 
     As shown, network device data storage  1274  may include a database, text storage, a spreadsheet, a folder or directory hierarchy, a file or files or the like that may be configured to maintain and store user account identifiers, user profiles, email addresses, IM addresses, and/or other network addresses or the like. Network device data storage  1274  may further include program code, data, algorithms and the like, for use by a processor such as a network device processor  1270  to execute and perform actions. In certain embodiments, at least some of the logical contents of network device data storage  1274  might be stored on another component of network device  12 C 01 , such as on a second instance of hard disk drive  1286  or on an external/removable storage device. 
     The network device data storage  1274  may further store any portions of application data and/or user data such as an application profile store  1275 , a web profile store  1276 , a profile enrichment store  1277  and/or any user data collected. In some embodiments, user data  1291  may store unique user data, non-unique user data, aggregated user data, and/or any combination thereof. User data  1291  may include a variety of attributes such as a five digit zip code, an expanded nine digit zip code and the like. 
     The Network device data storage  1274  may also store program code and data. One or more network device applications  1278  may be loaded into network device data storage or any other mass memory, to be accessible to run with or as a part of network device operating system  1273 . Examples of network device application programs may include transcoders, schedulers, calendars, database programs, word processing programs, hypertext transfer protocol (HTTP) programs, customizable user interface programs, IPSec applications, encryption programs, security programs, SMS message servers, IM message servers, email servers, account managers, and so forth. A messaging server  1292 , website server  1279 , user data aggregator server  1293 , a cross-domain multi-profile tracking server  1280 , and/or user data supplier server  1294  may also be included within or implemented as application programs. 
     A messaging server  1292  may include virtually any computing component or components configured and arranged to forward messages from message user agents and/or other message servers, or to deliver messages to a local message store such as network device data storage  1274  or the like. Thus, a messaging server  1292  may include a message transfer manager to communicate a message employing any of a variety of email protocols including, but not limited, to simple mail transfer protocol (SMTP), post office protocol (POP), Internet message access protocol (IMAP), network new transfer protocol (NNTP) or the like. A messaging server  1292  may also be managed by one or more components of the messaging server  1292 . Thus, the messaging server  1292  may also be configured to manage SMS messages; IM, MMS, IRC, or RSS feeds; mIRC; or any of a variety of other message types. In certain embodiments, the messaging server  1292  may enable users to initiate and/or otherwise conduct chat sessions, VOIP sessions or the like. 
     A website server  1279  may represent any of a variety of information and services that are configured to provide content, including messages, over a network to another computing device. Thus, a website server  1279  can include, for example, a web server, a file transfer protocol (FTP) server, a database server, a content server or the like. A website server  1279  may provide the content including messages over the network using any of a variety of formats including, but not limited to WAP, HDML, WML, SGML, HTML, XML, compact HTML (cHTML), extensible HTML (xHTML) or the like. A website server  1279  may also be configured to enable a user of a client device to browse websites, upload user data, view and interact with advertisements or the like. 
     A user data aggregator server  1293  is configured to aggregate user data to be provided to user data buyers for advertising campaigns. In certain embodiments, a user data aggregator server  1293  may be configured to receive collected user data from a user data supplier server  1294 . In some embodiments, a user data aggregator server  1293  may receive a query for user data. Based on the query, a user data aggregator server  1293  may generate a plurality of subsets of aggregated user data. In some embodiments, user data aggregator server  1293  may be included in a network device. 
     A user data supplier server  1294  is configured to collect user data. In certain embodiments, the user data supplier server  1294  may be configured to provide the collected user data to user data aggregator server  1293 . In some embodiments, the user data supplier server  1294  may collect and/or provide unique user data and/or non-unique user data. In certain embodiments, the user data supplier server  1294  may aggregate the collected user data. In some embodiments, the user data supplier server  1294  may be included in any computing device such as heretofore described. 
     Returning to discussion of the heretofore introduced environments, the environments include components with which various systems can be implemented. Not all of the components shown may be required to practice the embodiments, and variations in the arrangement and type of the components may be made without departing from the spirit or scope of the disclosure. 
     Various environments in which embodiments of the disclosure operate may include local area networks (LANs)/wide area networks (WANs), wireless networks, and/or client devices (e.g., user stations). The overall network including any sub-networks and/or wireless networks are in communication with, and enables communication between components in the environment. 
     Instances of client devices may include virtually any computing device capable of communicating over a network to send and receive information, including instant messages, performing various online activities or the like. It should be recognized that more or fewer client devices may be included within a system such as described herein, and embodiments are therefore not constrained by the number or type of client devices employed. 
     Devices that may operate as client devices may include devices that can connect using a wired or wireless communications medium such as personal computers, servers, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs or the like. In some embodiments, client devices may include virtually any portable computing device capable of connecting to another computing device and receiving information such as a laptop computer, a smart phone, a tablet computer, or the like. Portable or mobile computer devices are may also include or operate in conjunction with other portable devices such as cellular telephones, display pagers, radio frequency (RF) devices, infrared (IR) devices, personal digital assistants (PDAs), handheld computers, wearable computers integrated devices combining one or more of the preceding devices and the like. As such, client devices can range widely in terms of capabilities and features. Moreover, client devices may provide access to various computing applications including a browser or other web-based applications. A web-enabled client device may include a browser application that is configured to receive and to send web pages, web-based messages and the like. The browser application may be configured to receive and display graphics, text, multimedia and the like, employing virtually any web-based language including a wireless application protocol messages (WAP) and the like. In certain embodiments, the browser application is enabled to employ handheld device markup language (HDML), wireless markup language (WML), WMLScript, JavaScript, standard generalized markup language (SGML), HyperText markup language (HTML), eXtensible markup language (XML) and the like, to display and send a message. In certain embodiments, a user of the client device may employ the browser application to perform various activities over a network (online). However, another application may also be used to perform various online activities. 
     Client devices may include at least one client application that is configured to receive and/or send data between another computing device (e.g., a server component). The client application may include a capability to provide send and/or receive content or the like. The client application may further provide information that identifies itself including a type, capability, name or the like. In certain embodiments, a client device may uniquely identify itself through any of a variety of mechanisms including a phone number, mobile identification number (MIN), an electronic serial number (ESN), or other mobile device identifier. The information may also indicate a content format that the mobile device is enabled to employ. Such information may be provided in a network packet or the like, sent between other client devices, or sent between other computing devices. 
     Client devices may be further configured to include a client application that enables an end-user to log into an end-user account that may be managed by another computing device. Such end-user accounts, in one non-limiting example, may be configured to enable the end-user to manage one or more online activities including, in one non-limiting example, search activities, social networking activities, browse various websites, communicate with other users, participate in gaming, interact with various applications or the like. However, participation in online activities may also be performed without logging into the end-user account. 
     A wireless communication capability is configured to couple client devices and other components with network. Wireless network may include any of a variety of wireless sub-networks that may further overlay stand-alone and/or ad-hoc networks and the like, to provide an infrastructure-oriented connection for client devices. Such sub-networks may include mesh networks, wireless LAN (WLAN) networks, cellular networks and the like. In certain embodiments, the system may include more than one wireless network. 
     A wireless network may further include an autonomous system of terminals, gateways, routers, mobile network edge devices and the like which may be connected by wireless radio links, etc. Connections may be configured to move freely and randomly and organize themselves arbitrarily such that the topology of a wireless network may change rapidly. A wireless network may further employ a plurality of access technologies including AMPS and/or second generation (2G), and/or third generation (3G), and/or fourth generation (4G) generation radio access for cellular systems, WLAN, wireless router (WR) mesh and the like. The foregoing access technologies as well as emerging and/or future access technologies may enable wide area coverage for mobile devices such as client devices with various degrees of mobility. In one non-limiting example, wireless network may enable a radio connection through a radio network access such as a global system for mobile (GSM) communication, general packet radio services (GPRS), enhanced data GSM environment (EDGE), wideband code division multiple access (WCDMA) and the like. A wireless network may include any wireless communication mechanism by which information may travel between client devices and/or between another computing device and/or between other networks. 
     Any of the foregoing networks can be configured to couple network devices with other computing devices and communication can include communicating between the Internet. In some situations communication is carried out using combinations of LANs, WANs, as well as direct connections such as through a universal serial bus (USB) port, other forms of computer readable media. On an interconnected set of LANs, including those based on differing architectures and protocols, a router acts as a link between LANs, enabling messages to be sent from one to another. In addition, communication links within LANs may include twisted wire pair or coaxial cable, while communication links between networks may use analog telephone lines, full or fractional dedicated digital lines including T1, T2, T3, and T4, and/or other carrier mechanisms including, for example, E-carriers, integrated services digital networks (ISDNs), digital subscriber lines (DSLs), wireless links including satellite links, or other communications links known to those skilled in the art. Moreover, communication links may further employ any of a variety of digital signaling technologies including, without limit, for example, DS-0, DS-1, DS-2, DS-3, DS-4, OC-3, OC-12, OC-48 or the like. Furthermore, remote computers and other related electronic devices can be remotely connected to either LANs or WANs via a modem and temporary telephone link. In various embodiments, network  108  may be configured to transport information of an Internet protocol (IP). In some cases, communication media carries computer readable instructions, data structures, program modules, or other transport mechanism and includes any information delivery media. By way of example, communication media includes wired media such as twisted pair, coaxial cable, fiber optics, wave guides, and other wired media and wireless media such as acoustic, RF, infrared, and other wireless media. 
     In the foregoing specification, the disclosure has been described with reference to specific embodiments thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the disclosure. For example, the above-described process flows are described with reference to a particular ordering of process actions. However, the ordering of many of the described process actions may be changed without affecting the scope or operation of the disclosure. The specification and drawings are, accordingly, to be regarded in an illustrative sense rather than in a restrictive sense.