Patent Publication Number: US-2018039705-A1

Title: Method and system for analysis of user data based on social network connections

Description:
FIELD 
     The present disclosure relates to analyzing user data based on social network data and connections to the user, specifically the analysis of names associated with connections to a social network user and the attribution of demographic data to names to determine demographics of the user&#39;s connections on the social network. 
     BACKGROUND 
     Social networks have become extremely prevalent in the lives of many people. Social networks enable people to connect to others, share ideas, experiences, thoughts, etc., and to stay in touch with others and communicate with others on a large scale through the use of available technologies. While social networks often retain data regarding their users, this data may often be of little value to third parties. However, what data social networks do have available on users, such as their name and geographic location, may be useful when combined with other available data sources. Unfortunately, there is a lack of computing systems capable of combining social network data with other available data sources to make use of such information. 
     Thus, there is a need for a technical solution for the development of a computing system specifically programmed to be able to combined social network data with other available data sources to greatly increase the utility of such data. More specifically, the analysis of the names of users connected to a social network user as the names relate to demographics and other information may provide for a detailed look at the demographics over which the social network user has influence, which may be unavailable via any other method. As such, the analysis of names presents an opportunity that is currently unavailable using existing technological systems. 
     SUMMARY 
     The present disclosure provides a description of systems and methods for analyzing user data based on social network connections. 
     A method for analyzing user data based on social network connections includes: storing, in an association database of the processing server, a plurality of association profiles, wherein each association profile includes a structured data set related to a data association including at least a name, one or more demographic labels, and, for each demographic label, an associated demographic value; receiving, by a receiving device of the processing server, a data signal encoded with user data, wherein the user data is related to a user of a social network and includes at least a provided name for a plurality of connected users associated with the related user on the social network; executing, by a querying module of the processing server, a query on the associated database to identify, for each provided name included in the user data, a related association profile where the included name corresponds to the respective provided name; aggregating, by a data aggregation module of the processing server, for each demographic label, the associated demographic value included in each of the identified related association profiles to obtain, for each demographic label, one or more demographic metrics; and executing, by the querying module of the processing server, a query on a user database of the processing server to store, in the user database, a user profile, wherein the user profile includes a structured data set related to the user of the social network including at least each demographic label and associated one or more demographic metrics. 
     A system for analyzing user data based on social network connections includes: an association database of the processing server configured to store a plurality of association profiles, wherein each association profile includes a structured data set related to a data association including at least a name, one or more demographic labels, and, for each demographic label, an associated demographic value; a receiving device of the processing server configured to receive a data signal encoded with user data, wherein the user data is related to a user of a social network and includes at least a provided name for a plurality of connected users associated with the related user on the social network; a querying module of the processing server configured to execute a query on the associated database to identify, for each provided name included in the user data, a related association profile where the included name corresponds to the respective provided name; and a data aggregation module of the processing server configured to aggregate for each demographic label, the associated demographic value included in each of the identified related association profiles to obtain, for each demographic label, one or more demographic metrics. The querying module of the processing server is further configured to execute a query on a user database of the processing server to store, in the user database, a user profile, wherein the user profile includes a structured data set related to the user of the social network including at least each demographic label and associated one or more demographic metrics. 
     A non-transitory computer readable recording medium configured to store program code executable by a processing device of a computing system for analyzing user data based on social network connections, wherein the program code is configured to cause the computing system to: store, in an association database of the processing server, a plurality of association profiles, wherein each association profile includes a structured data set related to a data association including at least a name, one or more demographic labels, and, for each demographic label, an associated demographic value; receive, by a receiving device of the processing server, a data signal encoded with user data, wherein the user data is related to a user of a social network and includes at least a provided name for a plurality of connected users associated with the related user on the social network; execute, by a querying module of the processing server, a query on the associated database to identify, for each provided name included in the user data, a related association profile where the included name corresponds to the respective provided name; aggregate, by a data aggregation module of the processing server, for each demographic label, the associated demographic value included in each of the identified related association profiles to obtain, for each demographic label, one or more demographic metrics; and execute, by the querying module of the processing server, a query on a user database of the processing server to store, in the user database, a user profile, wherein the user profile includes a structured data set related to the user of the social network including at least each demographic label and associated one or more demographic metrics. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING FIGURES 
       The scope of the present disclosure is best understood from the following detailed description of exemplary embodiments when read in conjunction with the accompanying drawings. Included in the drawings are the following figures: 
         FIG. 1  is a block diagram illustrating a high level system architecture for analyzing user data based on social network connections and name and demographic associations in accordance with exemplary embodiments. 
         FIG. 2  is a block diagram illustrating the processing server of  FIG. 1  for the analysis of name and demographic associations for connections to a social network user in accordance with exemplary embodiments. 
         FIG. 3  is a flow diagram illustrating a process for analyzing demographics of connections to a social network user based on name and demographic associations using the system of  FIG. 1  in accordance with exemplary embodiments. 
         FIG. 4  is a flow diagram illustrating a process for identifying social network users with target demographic influences based on connection names using the system of  FIG. 1  in accordance with exemplary embodiments. 
         FIG. 5  is a diagram illustrating a report of demographics of connections to a social network user based on name and demographic associations in accordance with exemplary embodiments. 
         FIG. 6  is a flow chart illustrating an exemplary method for analyzing user data based on social network connections in accordance with exemplary embodiments. 
         FIG. 7  is a block diagram illustrating a computer system architecture in accordance with exemplary embodiments. 
     
    
    
     Further areas of applicability of the present disclosure will become apparent from the detailed description provided hereinafter. It should be understood that the detailed description of exemplary embodiments are intended for illustration purposes only and are, therefore, not intended to necessarily limit the scope of the disclosure. 
     DETAILED DESCRIPTION 
     System for Analysis of Social Network User Data Based on Connection Names 
       FIG. 1  illustrates a system  100  for the analysis of user data for a social network user to determine the demographics of connected users based on associations between names and demographics for connections to the social network user. 
     The system  100  may include a processing server  102 . The processing server  102 , discussed in more detail below, may be configured to analyze user data associated with a user  104  of a social network  106  to determine the demographics of a plurality of connections  108  to the user  104  on the social network  106 . The social network  106  may be any suitable type of social network that registers users and that collects at least a name for their registered users, such as Facebook®, Twitter®, Instagram®, LinkedIn®, etc. The name associated with each user  104  and connection  108  of the social network  106  may be a family name, last name, or surname, first name or given name, middle name, a combination thereof, or any other type of name that may be suitable for performing the functions discussed herein. 
     As part of the registration process for the social network  106 , the social network  106  may collect user data associated with each registrant. The user data may include at least the user&#39;s name and communication data suitable for use in communicating with the respective user. The communication data may include, for instance, an e-mail address, telephone number, device identifier, etc. The user  104  may register with the social network  106 , providing their registration information. The user  104  may then connect with a plurality of connections  108  using the social network  106 . Each connection  108  may be another user of the social network  106  with whom the user  104  wishes to connect, or vice versa. For instance, the user  104  may select one or more connections  108  for which the user  104  desires to see content from or share content with using the social network  106 . In another instance, connections  108  may select the user  104  to view content shared by the user  104  using the social network  106 . In some cases, a social network  106  may or may not require mutual acceptance to establish a connection between the user  104  and a connection  108 . For example, a connection  108  may “follow” (e.g., subscribe to content shared by the user  104 ) the user  104  without requiring the user  104  to consent to the action. 
     The processing server  102  may be configured to analyze the connections  108  for the user  104  to determine the demographics of the group of connections  108 . The processing server  102  may receive connection information for the user  104  from the social network  106 . In some embodiments, the processing server  102  may electronically transmit a data signal encoded with a request to the social network  106 , where the request may indicate the user  104  for which data is requested. In other embodiments, the social network  106  may electronically transmit data signals to the processing server  102  encoded with user data for one or more users  104  for use in performing the functions discussed herein. In some cases, the processing server  102  may be a part of the social network  106  and may perform the actions discussed herein using user data stored internally or otherwise accessible to the social network  106 . The user data may include at least a name for a plurality of connections  108  associated with the user  104 . 
     The processing server  102  may receive name and demographic associations from one or more data collection agencies  110  included in the system  100 . The data collection agencies  110  may be entities configured to collect data regarding associations between names and demographics. Demographics may include age, age range, birth year, birth year range, gender, ethnicity, nationality, geographic location, and any other type of characteristic associated with demographics. In some instances, these characteristics may include education, residential status, marital status, familial status, occupation, income, etc. In some cases, some demographic characteristics may not be directly associated with a name. Data collection agencies  110  may directly associate a name to one or more demographic characteristics, based on a prevalence of a connection between the name and respective characteristic. For example, the name “Liam” may be associated with males born between the years 2011-2014, due to its popularity as a name for males during that time period, and lack of popularity in other time periods. In a further example, the name “Liam” may be associated with the United States, Canada, and Great Britain due to its popularity in those countries and a lack of popularity in other countries. The data collection agencies  110  may collect data regarding such associations between names and demographics, and may collect the data using suitable collection methods, which may include the consulting of one or more additional sources. For example, the Social Security Administration may be a data collection agency  110  or may provide data to one or more data collection agencies  110  for generation of the associations used herein. 
     Each data collection agency  110  may electronically transmit data signals encoded with name and demographic association data to the processing server  102  using a suitable communication network and method. Suitable communication networks may include, for example, the Internet, local area networks, wireless area networks, radio frequency networks, cellular communication networks, etc. The processing server  102  may receive the name and demographic associations and may store them in a locally stored or otherwise accessible database, such as an association database, discussed in more detail below. The processing server  102  may then identify the demographics associated with each name for the connections  108  associated with a user  104  for which user data is received from the social network  106 . 
     Such analysis may include identifying the demographics associated with each individual name of the connections  108  for the user  104 , and then the aggregation of the demographics. For example, the processing server  102  may identify a gender associated with each name for the connections  108  and then may aggregate the genders to determine a percentage of connections  108  for the user  104  associated with each gender. The processing server  102  may repeat the process for each demographic characteristic, such as determining percentages for age and/or birth year, ethnicity, nationality, etc. In some instances, the processing server  102  may only aggregate the connections  108  for which such demographics are available. In some cases, the number of connections  108  aggregated for each characteristic may be different. For example, the user  104  may have 100 connections. Gender information may be available for each name for the 100 connections, but ethnicity information may only be available for 70 of the connections (e.g., due to a lack of available data for the name, no specific association for the name, etc.). In such an instance, the gender percentages may be based on all 100 connections, while ethnicity information may be based on the 70 connections for which data is available. 
     The processing server  102  may perform the aggregation and may represent the demographics for the connections  108  for the user  104  as percentages, ratios, or other suitable type of representation. In some instances, the processing server  102  may identify the most common value for each demographic characteristic. For example, the processing server  102  may analyze a user  104  to determine the most common gender, ethnicity, and nationality of their connections  108 . In some cases, the processing server  102  may identify the percentages or rates of specifically requested demographics, such as identifying what portion of connections  108  for a user  104  are located in a specified country. 
     Once the demographics for the connections  108  of a user  104  have been identified, the processing server  102  may store the data in a locally stored or otherwise accessible database, such as a user database, discussed in more detail below. In some embodiments, the system  100  may include a data requester  112 . The data requester  112  may electronically transmit a data signal to the processing server  102  via a suitable communication network that is encoded with a demographic request. The demographic request may indicate a user  104  for which the demographics of that user&#39;s connections  108  is requested. The processing server  102  may identify the demographics, such as discussed above, and may electronically transmit a return data signal to the data requester  112  that is encoded with the identified demographic data. 
     In some embodiments, a data requester  112  may request users that have connections  108  matching one or more specified criteria. For example, the data requester  112  may request users  104  whose connections  108  are at least 70% female, have at least 50% between the ages of 16 and 35, and that are primarily located in North America. The processing server  102  may identify the demographics identified for a plurality of different users  104 , such as stored in the user database, and may identify users  104  whose connection demographics match the specified criteria. The processing server  102  may then provide the users to the data requester  112 . The data requester  112  may then reach out to the users  104  (e.g., as may be subject to terms and conditions set forth by the social network  106 , users  104 , connections  108 , etc.) accordingly. For example, an advertising agency may contact a user  104  due to their connections  108  matching the target market for an advertisement, to broker a deal to have the user  104  share an advertisement or other suitable content to their connections  108 . 
     In some embodiments, user data provided by the social network  106  may include data for each connection  108  in addition to their provided name. For example, connections  108  may also provide a geographic location, age, gender, ethnicity, nationality, or other demographic value to the social network  106  as part of the registration process. The social network  106  may provide this data to the processing server  102  in addition to the name. Such data may be used in the identification of demographic characteristics for each connection  108 . For example, the social network  106  may provide a name and geographic location for each connection  108 . The processing server  102  may then identify demographic characteristics based on associations between demographics and a combination of name and geographic location, as provided by the data collection agencies  110 . For instance, the name “Ashley” may be primarily associated with the female gender in the United States, but may be primarily associated with the male gender in England. In such an instance, the demographics associated with the name “Ashley” may differ based on the geographic location. 
     In such embodiments, the processing server  102  may use a similar process as discussed above to identify demographics associated with connections  108  for a user  104 , but may use different name and demographic associations, which may rely on a combination of name and other data provided by the social network  106  for each of the connections  108 . In some embodiments, the processing server  102  may specifically request demographic associations from the data collection agencies  110  based on data provided by the social network  106 . For example, if the social network  106  provides user data to the processing server  102  that includes a name and age range for each connection  108 , the processing server  102  may request demographics associated with a combination of name and age range from the data collection agencies  110 , and may then match those demographics to the connections  108  based on their names and age ranges accordingly. 
     Methods and systems discussed herein may enable the processing server  102  to identify the demographics for connections  108  associated with a user  104  of a social network  106  based on associations between demographics and names. By way of the specialized programming and configurations of the technical systems of the processing server  102  discussed herein, the processing server  102  may be configured to associate demographics to each of the connections  108 , to aggregate the demographics to identify an approximate demographic analysis of the users associated with a specific user  104  of a social network  106 . Such information may be beneficial for use in a variety of technologies and industries. 
     For example, an advertiser desiring to reach a specific demographic market may be able to identify ideal users  104  to serve as sponsors or spokespersons for advertised content. In another example, a user  104  may share negative content associated with a restaurant, such as a bad review of a dining experience, and the restaurant may be able to identify the demographics of the connections  108  to that user  104  via the processing server  102  to identify a suitable reaction to the user&#39;s bad experience. In yet another example, a politician running for an elected office may identify target users  104  for an endorsement based on the demographics of their connection, or may identify the demographics of connections  108  for users  104  that support their race for office as part of their campaign strategy. 
     Processing Server 
       FIG. 2  illustrates an embodiment of the processing server  102  of the system  100 . It will be apparent to persons having skill in the relevant art that the embodiment of the processing server  102  illustrated in  FIG. 2  is provided as illustration only and may not be exhaustive to all possible configurations of the processing server  102  suitable for performing the functions as discussed herein. For example, the computer system  700  illustrated in  FIG. 7  and discussed in more detail below may be a suitable configuration of the processing server  102 . 
     The processing server  102  may include a receiving device  202 . The receiving device  202  may be configured to receive data over one or more networks via one or more network protocols. In some instances, the receiving device  202  may be configured to receive data from social networks  106 , data collection agencies  110 , data requesters  112 , and other entities via alternative networks, such as the Internet. In some embodiments, the receiving device  202  may be comprised of multiple devices, such as different receiving devices for receiving data over different networks, such as a first receiving device for receiving data over a cellular communication network and a second receiving device for receiving data over the Internet. The receiving device  202  may receive electronically data signals that are transmitted, where data may be encoded in the data signal and decoded, parsed, read, or otherwise obtained via receipt of the data signal by the receiving device  202 . In some instances, the receiving device  202  may include a parsing module for parsing the received data signal to obtain the data encoded therein. For example, the receiving device  202  may include a parser program configured to receive and transform the received data signal into usable input for the functions performed by the processing device to carry out the methods and systems described herein. 
     The receiving device  202  may be configured to receive data signals electronically transmitted by data collection agencies  110  encoded with name and demographic associations. In some instances, the demographic characteristics may be associated with names and additional values, such as a combination of name and geographic location. The receiving device  202  may also be configured to receive data signals electronically transmitted by social networks  106  that are encoded with user data. The user data may include names and any additional data associated with connections  108  connected to a user  104  of the social network  106 . The receiving device  202  may also be configured to receive data signals electronically transmitted by data requesters  112 , which may be encoded with data requests. The data requests may request demographic information for a specific user  104  or users of a social network  106 , or may be request users  104  of social networks  106  whose connections  108  match specified demographic criteria. 
     The processing server  102  may also include a communication module  204 . The communication module  204  may be configured to transmit data between modules, engines, databases, memories, and other components of the processing server  102  for use in performing the functions discussed herein. The communication module  204  may be comprised of one or more communication types and utilize various communication methods for communications within a computing device. For example, the communication module  204  may be comprised of a bus, contact pin connectors, wires, etc. In some embodiments, the communication module  204  may also be configured to communicate between internal components of the processing server  102  and external components of the processing server  102 , such as externally connected databases, display devices, input devices, etc. The processing server  102  may also include a processing device. The processing device may be configured to perform the functions of the processing server  102  discussed herein as will be apparent to persons having skill in the relevant art. In some embodiments, the processing device may include and/or be comprised of a plurality of engines and/or modules specially configured to perform one or more functions of the processing device, such as a querying module  214 , data aggregation module  216 , analytical module  218 , etc. As used herein, the term “module” may be software or hardware particularly programmed to receive an input, perform one or more processes using the input, and provide an output. The input, output, and processes performed by various modules will be apparent to one skilled in the art based upon the present disclosure. 
     The processing server  102  may include an association database  206 . The association database  206  may be configured to store a plurality of association profiles  208  using a suitable data storage format and schema. The association database  206  may be a relational database that utilizes structured query language for the storage, identification, modifying, updating, accessing, etc. of structured data sets stored therein. Each association profile  208  may be a structured data set configured to store data related to a data association between names and one or more demographic characteristics. Each association profile  208  may include at least a name and one or more demographic labels and, for each demographic label, one or more associated demographic values. Demographic labels may include, for example, gender, age, age range, birth year, birth year range, ethnicity, nationality, geographic location, etc. Demographic values may include suitable values for the corresponding demographic label such as, for the label of gender, male or female. 
     The processing server  102  may also include a user database  210 . The user database  210  may be configured to store a plurality of user profiles  212  using a suitable data storage format and schema. The user database  210  may be a relational database that utilizes structured query language for the storage, identification, modifying, updating, accessing, etc. of structured data sets stored therein. Each user profile  212  may be a structured data set configured to store data related to a user  104  of a social network  106 . The user profile  212  may include user data associated with that user  104  provided by the social network  106 , such as communication data, a user identification value, provided name, geographic location, etc., as well as data associated with connections  108  of the user  104 , such as connection names. The user profile  212  may also include demographic data identified by the processing server  102  using the methods discussed herein for demographics associated with connections  108  of the user  104 . 
     The processing server  102  may include a querying module  214 . The querying module  214  may be configured to execute queries on databases to identify information. The querying module  214  may receive one or more data values or query strings, and may execute a query string based thereon on an indicated database, such as the association database  206  and user database  210 , to identify information stored therein. The querying module  214  may then output the identified information to an appropriate engine or module of the processing server  102  as necessary. The querying module  214  may, for example, execute a query on the association database  206  to identify an association profile  208  for a name associated with a connection  108 , for identification of the demographic values associated therewith. The querying module  214  may also be configured to execute a query on the user database  210  to identify a user profile  212  that matches a specified user or specified connection demographic criteria as requested by a data requester  112  (e.g., and received via the receiving device  202 ). 
     The processing server  102  may also include a data aggregation module  216 . The data aggregation module  216  may be configured to aggregate data identified via the querying module  214 . The data aggregation module  216  may receive a plurality of association profiles  208  and/or user profiles  212  or data included therein, may aggregate the data as per a received request, and may output the aggregated data. For example, the data aggregation module  216  may aggregate demographic values for one or more demographic labels for a plurality of association profiles  208  and/or the demographic values included therein for connections  108  for a user  104 . The data aggregation module  216  may output the aggregated data to the querying module  214  for use in inserting into a related user profile  212  via a query on the user database  210 , and/or may output the data to another module or engine of the processing server  102 , such as for providing to a data requester  112  in response to a received request. 
     In some embodiments, the processing server  102  may also include an analytical module  218 . The analytical module  218  may be configured to perform any additional analysis regarding connections  108  for a user  104  and data associated therewith. For instance, the analytical module  218  may be programmed to identify an ideal user  104  for specified demographic criteria provided by a data requester  112 . For example, the data requester  112  may submit (e.g., as received by the receiving device  202 ) a request for a user  104  that has connections  108  with a high percentage of women between the ages of 25 and 39 located in North America. The querying module  214  may identify a plurality of user profiles  212  related to users that meet the criteria, and may pass them to the analytical module  218  for analysis and selection. The analytical module  218  may be configured to select a single user profile  212 , such as based on weighted considerations of the criteria, number of connections  108 , percentages of each criteria, etc., as may be set by the data requester  112  or one or more algorithms stored in the processing server  102 . For instance, in the above example, the analytical module  218  may select a user  104  with a smaller percentage of women in the specified age range and location, but with such a large number of connections  108  that yields a greater target audience. 
     In some embodiments, the analytical module  218  may be configured to analyze association profiles  208  and name-year associations to estimate an age for the name of a connection  108 . In one embodiment, the analytical module  218  may analyze the number of times a name was listed as a birth name in any given year (e.g., as received from data collection agencies  110 ). The analytical module  218  may calculate a percentage of total occurrences of that name for each year based on the total number of times the name was listed for all of the years as a total. The analytical module  218  may then multiply that percentage by the age of individuals born in that respective year, and sum the results to calculate an estimated age for individuals with that name. 
     The processing server  102  may also include a transmitting device  220 . The transmitting device  220  may be configured to transmit data over one or more networks via one or more network protocols. In some embodiments, the transmitting device  220  may be configured to transmit data to social networks  106 , data collection agencies  110 , data requesters  112 , and other entities via suitable communication networks, such as the Internet. In some embodiments, the transmitting device  220  may be comprised of multiple devices, such as different transmitting devices for transmitting data over different networks, such as a first transmitting device for transmitting data over a cellular communication network and a second transmitting device for transmitting data over the Internet. The transmitting device  220  may electronically transmit data signals that have data encoded that may be parsed by a receiving computing device. In some instances, the transmitting device  220  may include one or more modules for encoding or otherwise formatting data into data signals suitable for transmission. 
     The transmitting device  220  may be configured to electronically transmit data signals to social networks  106  that are encoded with user data requests. The user data requests may specify a user  104  or users  104  for which user data, that includes names of connections  108 , is requested. The transmitting device  220  may also be configured to electronically transmit data signals to data collection agencies  110  that are encoded with name and demographic association requests. In some instances, such requests may also include additional characteristics for which demographics are to be associated, such as a combination of name and geographic location. The transmitting device  220  may also be configured to electronically transmit data signals to data requesters  112 , such as may be encoded with responses to received data requests, such as including demographic characteristics or user profiles  212  requested by the data requester  112 . 
     The processing server  102  may also include a memory  222 . The memory  222  may be configured to store data for use by the processing server  102  in performing the functions discussed herein. The memory  222  may be configured to store data using suitable data formatting methods and schema and may be any suitable type of memory, such as read-only memory, random access memory, etc. The memory  222  may include, for example, encryption keys and algorithms, communication protocols and standards, data formatting standards and protocols, program code for modules and application programs of the processing device, and other data that may be suitable for use by the processing server  102  in the performance of the functions disclosed herein as will be apparent to persons having skill in the relevant art. 
     As discussed above, the processing server  102  may be specifically configured to perform the functions discussed herein for the analysis of user data and identification of demographic characteristics associated with connections  108  for a user  104  of a social network  106 . As part of the specialized configuration, the processing server  102  may store (e.g., in the memory  222 ) a plurality of functions for execution by the processing unit and various modules or engines of the processing server  102  for performing the actions of the processing server  102  discussed herein. For example, the memory  222  may include a get_account_data(account_url) function, which may be configured to scrape account information from the social network  106  page for a specified user  104 , based on the account_url provided. Such a function may operate as a request for the user data, where the account_url may indicate the user  104  for which the data is requested. 
     The memory  222  may also include a is_boy_or_girl(first_name) function, which may check a specified name (e.g., as associated with a connection  108  to the user  104 ) for association with a male or female gender. The function may include, for instance, the generation and execution of a query by the querying module  214  on the association database  206  to identify an association profile  208  that includes the provided first_name value, and the identification of the gender value stored for the gender demographic label in the association profile  208 . Additional functions that may be stored in the memory  222  and executed by the processing server  102  to perform the functions discussed herein may include functions for identifying an age, age range, birth year, or birth year range for a specified name, identifying ethnicity for a specified name (e.g., given name or surname), identifying a number of connections  108  for a user  104 , the building of tables for representations of identified demographic characteristics, calculation of percentages of demographic characteristics, etc. 
     Process for Analyzing User Data to Identify Connected Demographic Characteristics 
       FIG. 3  illustrates a process for the analysis of user data for the user  104  of a social network  106  for the identification of demographic characteristics for connections  108  associated with the user  104  on the social network  106 . 
     In step  302 , the processing server  102  may store a plurality of association profiles  208  in the association database  206 , where each association profile  208  includes at least a name, one or more demographic labels, and, for each demographic label, at least one demographic value associated with the respective name. The data stored in each association profile  208  may be based on data received by the receiving device  202  of the processing server  102  from the data collection agencies  110 . In step  304 , the data requester  112  may electronically transmit a data signal to the processing server  102  using a suitable communication network and method, where the data signal is encoded with a request for demographic data. The request for demographic data may include at least an identification value associated with a user  104  for which the demographic data is requested. The identification value may be, for example, a name, username, email address, telephone number, identification number, device identifier, or other suitable value. In some embodiments, the request for demographic data may also include one or more demographic labels for which demographic characteristics are requested. 
     In step  306 , the receiving device  202  of the processing server  102  may receive and parse the request for demographic data. In step  308 , the transmitting device  220  of the processing server  102  may electronically transmit a data signal to the social network  106  that is encoded with a request for connection data. The request for connection data may include at least the identification value associated with the user  104  for which data is requested, and may request at least the name provided for each connection  108  connected to the user  104  on the social network  106 . In step  310 , the social network  106  may receive the request for connection data. 
     In step  312 , the social network  106  may identify each of the users connected to the user  104  that corresponds to the provided identification value on the social network  106 , referred to herein as connections  108  to the user  104 . In step  314 , the social network  106  may electronically transmit a data signal back to the processing server  102  using a suitable communication network and method that is encoded with connection data that includes at least the name provided for each of the identified connections  108 . In some instances, the connection data may also include additional data provided by each connection  108 , such as a geographic location, age, age range, nationality, etc. In step  316 , the receiving device  202  of the processing server  102  may receive and parse the connection data. 
     In step  318 , the querying module  214  of the processing server  102  may execute queries on the association database  206  of the processing server  102  to identify association profiles  208  for each of the provided names for the connections  108  as included in the received connection data. In step  320 , the data aggregation module  216  of the processing server  102  may aggregate the demographic values for each of the demographic labels included in each of the association profiles  208  identified by the querying module  214  in step  318 . The result may be an aggregation of demographic values for each demographic label for all of the connections  108  connected to the user  104  on the social network  106 . In step  322 , the transmitting device  220  of the processing server  102  may electronically transmit a data signal to the data requester  112  using a suitable communication network and method that is encoded with at least the aggregated demographic values. In step  324 , the data requester  112  may receive their requested demographic data. 
     Process for Identifying Demographically Suitable Users Based on User Data Analysis 
       FIG. 4  illustrates a process for the identification of one or more users  104  of a social network  106  whose connections  108  fit a specified demographic profile as requested based on user data analysis done for the connections  108  for each user  104  of the social network  106 . 
     In step  402 , the processing server  102  may store name association data as association profiles  208  in the association database  206  of the processing server  102 . Each association profile  208  may include at least a name, one or more demographic labels, and, for each demographic label, at least one demographic value associated with the respective name. The data stored in each association profile  208  may be based on data received by the receiving device  202  of the processing server  102  from the data collection agencies  110 . In some instances, each association profile  208  may include additional data, such as a demographic value or characteristic associated with the name to which the included demographic labels and values apply. 
     In step  404 , the processing server  102  may store user demographic data as a plurality of user profiles  212  in the user database  210  of the processing server  102 . Each user profile  212  may include data related to a user  104  of a social network  106  and may include an identification value for the respective user  104  and aggregated demographic characteristics for the connections  108  of the respective user  104  as identified by the processing server  102  using the methods discussed herein. 
     In step  406 , the data requester  112  may electronically transmit a data signal to the processing server  102  using a suitable communication network and method that is encoded with a request for eligible users. The request for eligible users may include at least one or more desired criteria for demographic values of a user  104  of a social network  106 . In some instances, the request may specify the social network  106  or additional criteria, such as number of connections  108  for the desired user. The desired criteria for demographic values may include percentages or other representations of the demographic values and may include additional criteria associated therewith. For example, a data requester  112  may request users  104  whose connections  108  are at least 75% female, with the number of females being at least 10,000, based on the identified demographics percentage for that user&#39;s connections  108  and the user&#39;s number of connections  108 . In step  408 , the receiving device  202  of the processing server  102  may receive and parse the request. 
     In step  410 , the querying module  214  of the processing server  102  may execute a query on the user database  210  to identify one or more user profiles  212  where the included demographic values satisfy the criteria set forth in the request for eligible users. In some embodiments, the analytical module  218  of the processing server  102  may be configured to perform analysis on the identified user profiles  212 , such as to select one or more user profiles  212  from the identified set, which may be based on additional criteria included in the request for eligible users. 
     In step  412 , the transmitting device  220  of the processing server  102  may electronically transmit a data signal to the data requester  112  using a suitable communication network and methods that is encoded with at least the identification value included in each of the identified user profiles  212 . In some instances, the user profiles  212  themselves or additional data included therein may be included in the data provided to the data requester  112 , such as for use by the data requester  112  in further selection of users  104 . In step  414 , the data requester  112  may receive the data regarding the identified users  104  and, in step  416 , may contact suitable users  104 , such as to seek assistance in promoting a product, purchasing a product, voting for a candidate, etc. 
     Demographic Values as Analysis of User Data 
       FIG. 5  illustrates demographic values for connections  108  for a user  104  of a social network  104  as identified by the processing server  102  using the methods discussed herein. For example, the table  500  illustrated in  FIG. 5  and discussed below may be provided to a data requester  112  in response to a request for demographic characteristics for a specific user  104 , as performed in the process illustrated in  FIG. 3  and discussed above. 
     As illustrated in the table  500 , the processing server  102  may be configured to identify and aggregate demographic values for one or more demographic labels for a user  104  of a social network  106  as related to the connections  108  of that user  104 , based on the names provided by the connections  108  to the social network  106 . In the illustrated example, the processing server  102  has analyzed the gender, country, and age range for each of the connections  108  for a specified user  104  of a social network  106 . 
     In the illustrated example, the processing server  102  has determined, based on the names of the connections  108  for the user  104 , that 18% of the user&#39;s connections  108  on the social network  106  are male, and 82% are female. A majority of the user&#39;s connections  108  live in the United States and are between the ages of 18 and 25. Such an analysis may reveal that the user  104  may be beneficial for use in reaching a target market of women up to 35 years old that live in North America, as up to 82% of the user&#39;s connections  108  meet that criteria, depending on how many of the 82% of the women fall into the 83% living in North America and 96% under 35. 
     Exemplary Method for Analyzing User Data Based on Social Network Connections 
       FIG. 6  illustrates a method  600  for the analysis of user data based on social network connections for identifying the demographic characteristics of connections to a user of a social network based on names provided for that user&#39;s connections and associations of names to demographic values. 
     In step  602 , a plurality of association profiles (e.g., association profiles  208 ) may be stored in an association database (e.g., the association database  206 ) of a processing server (e.g., the processing server  102 ), wherein each association profile includes a structured data set related to a data association including at least a name, one or more demographic labels, and, for each demographic label, an associated demographic value. In step  604 , a data signal encoded with user data may be received by a receiving device (e.g., the receiving device  202 ) of the processing server  102 , wherein the user data is related to a user (e.g., the user  104 ) of a social network (e.g., the social network  106 ) and includes at least a provided name for a plurality of connected users (e.g., connections  108 ) associated with the related user on the social network. 
     In step  606 , a query may be executed on the associated database by a querying module (e.g., the querying module  214 ) of the processing server to identify, for each provided name included in the user data, a related association profile where the included name corresponds to the respective provided name. In step  608 , the associated demographic value included in each of the identified related association profiles may be aggregated by a data aggregation module (e.g., the data aggregation module  216 ) of the processing server to obtain, for each demographic label, one or more demographic metrics. In step  610 , a query may be executed on a user database (e.g., the user database  210 ) of the processing server by the querying module of the processing server to store, in the user database, a user profile, wherein the user profile includes a structured data set related to the user of the social network including at least each demographic label and associated one or more demographic metrics. 
     In one embodiment, the method  600  may further include: receiving, by the receiving device of the processing server, a data signal encoded with a user information request, wherein the user information request specifies the user of the social network; and electronically transmitting, by a transmitting device (e.g., the transmitting device  220 ) of the processing server, a data signal encoded with at least each demographic label and associated one or more demographic metrics in response to the received data signal. In a further embodiment, the method  600  may also include electronically transmitting, by the transmitting device of the processing server, a data signal encoded with a user data request to the social network, wherein the user data request includes at least a user identifier associated with the user of the social network, and the data signal encoded with the user data is received in response to the user data request. In an even further embodiment, the user identifier may be included in the user information request. 
     In some embodiments, each association profile may further include a geographic location, the user data may further include a provided location for each of the plurality of connected users, and the related association profile identified for each provided name included in the user data may include a geographic location that corresponds to the provided location associated with the respective provided name. In one embodiment, each association profile may further include an age range, the user data may further include a provided age for each of the plurality of connected users, and the related association profile identified for each provided name included in the user data may include an age range that encompasses the provided age associated with the respective provided name. In some embodiments, the one or more demographic labels may include at least one of: age, gender, geographic location, ethnicity, income, education, occupation, residential status, familial status, and marital status. 
     Computer System Architecture 
       FIG. 7  illustrates a computer system  700  in which embodiments of the present disclosure, or portions thereof, may be implemented as computer-readable code. For example, the processing server  102  of  FIG. 1  may be implemented in the computer system  700  using hardware, software, firmware, non-transitory computer readable media having instructions stored thereon, or a combination thereof and may be implemented in one or more computer systems or other processing systems. Hardware, software, or any combination thereof may embody modules and components used to implement the methods of  FIGS. 3, 4, and 6 . 
     If programmable logic is used, such logic may execute on a commercially available processing platform or a special purpose device. A person having ordinary skill in the art may appreciate that embodiments of the disclosed subject matter can be practiced with various computer system configurations, including multi-core multiprocessor systems, minicomputers, mainframe computers, computers linked or clustered with distributed functions, as well as pervasive or miniature computers that may be embedded into virtually any device. For instance, at least one processor device and a memory may be used to implement the above described embodiments. 
     A processor unit or device as discussed herein may be a single processor, a plurality of processors, or combinations thereof. Processor devices may have one or more processor “cores.” The terms “computer program medium,” “non-transitory computer readable medium,” and “computer usable medium” as discussed herein are used to generally refer to tangible media such as a removable storage unit  718 , a removable storage unit  722 , and a hard disk installed in hard disk drive  712 . 
     Various embodiments of the present disclosure are described in terms of this example computer system  700 . After reading this description, it will become apparent to a person skilled in the relevant art how to implement the present disclosure using other computer systems and/or computer architectures. Although operations may be described as a sequential process, some of the operations may in fact be performed in parallel, concurrently, and/or in a distributed environment, and with program code stored locally or remotely for access by single or multi-processor machines. In addition, in some embodiments the order of operations may be rearranged without departing from the spirit of the disclosed subject matter. 
     Processor device  704  may be a special purpose or a general purpose processor device specifically configured to perform the functions discussed herein. The processor device  704  may be connected to a communications infrastructure  706 , such as a bus, message queue, network, multi-core message-passing scheme, etc. The network may be any network suitable for performing the functions as disclosed herein and may include a local area network (LAN), a wide area network (WAN), a wireless network (e.g., WiFi), a mobile communication network, a satellite network, the Internet, fiber optic, coaxial cable, infrared, radio frequency (RF), or any combination thereof. Other suitable network types and configurations will be apparent to persons having skill in the relevant art. The computer system  700  may also include a main memory  708  (e.g., random access memory, read-only memory, etc.), and may also include a secondary memory  710 . The secondary memory  710  may include the hard disk drive  712  and a removable storage drive  714 , such as a floppy disk drive, a magnetic tape drive, an optical disk drive, a flash memory, etc. 
     The removable storage drive  714  may read from and/or write to the removable storage unit  718  in a well-known manner. The removable storage unit  718  may include a removable storage media that may be read by and written to by the removable storage drive  714 . For example, if the removable storage drive  714  is a floppy disk drive or universal serial bus port, the removable storage unit  718  may be a floppy disk or portable flash drive, respectively. In one embodiment, the removable storage unit  718  may be non-transitory computer readable recording media. 
     In some embodiments, the secondary memory  710  may include alternative means for allowing computer programs or other instructions to be loaded into the computer system  700 , for example, the removable storage unit  722  and an interface  720 . Examples of such means may include a program cartridge and cartridge interface (e.g., as found in video game systems), a removable memory chip (e.g., EEPROM, PROM, etc.) and associated socket, and other removable storage units  722  and interfaces  720  as will be apparent to persons having skill in the relevant art. 
     Data stored in the computer system  700  (e.g., in the main memory  708  and/or the secondary memory  710 ) may be stored on any type of suitable computer readable media, such as optical storage (e.g., a compact disc, digital versatile disc, Blu-ray disc, etc.) or magnetic tape storage (e.g., a hard disk drive). The data may be configured in any type of suitable database configuration, such as a relational database, a structured query language (SQL) database, a distributed database, an object database, etc. Suitable configurations and storage types will be apparent to persons having skill in the relevant art. 
     The computer system  700  may also include a communications interface  724 . The communications interface  724  may be configured to allow software and data to be transferred between the computer system  700  and external devices. Exemplary communications interfaces  724  may include a modem, a network interface (e.g., an Ethernet card), a communications port, a PCMCIA slot and card, etc. Software and data transferred via the communications interface  724  may be in the form of signals, which may be electronic, electromagnetic, optical, or other signals as will be apparent to persons having skill in the relevant art. The signals may travel via a communications path  726 , which may be configured to carry the signals and may be implemented using wire, cable, fiber optics, a phone line, a cellular phone link, a radio frequency link, etc. 
     The computer system  700  may further include a display interface  702 . The display interface  702  may be configured to allow data to be transferred between the computer system  700  and external display  730 . Exemplary display interfaces  702  may include high-definition multimedia interface (HDMI), digital visual interface (DVI), video graphics array (VGA), etc. The display  730  may be any suitable type of display for displaying data transmitted via the display interface  702  of the computer system  700 , including a cathode ray tube (CRT) display, liquid crystal display (LCD), light-emitting diode (LED) display, capacitive touch display, thin-film transistor (TFT) display, etc. 
     Computer program medium and computer usable medium may refer to memories, such as the main memory  708  and secondary memory  710 , which may be memory semiconductors (e.g., DRAMs, etc.). These computer program products may be means for providing software to the computer system  700 . Computer programs (e.g., computer control logic) may be stored in the main memory  708  and/or the secondary memory  710 . Computer programs may also be received via the communications interface  724 . Such computer programs, when executed, may enable computer system  700  to implement the present methods as discussed herein. In particular, the computer programs, when executed, may enable processor device  704  to implement the methods illustrated by  FIGS. 3, 4, and 6 , as discussed herein. Accordingly, such computer programs may represent controllers of the computer system  700 . Where the present disclosure is implemented using software, the software may be stored in a computer program product and loaded into the computer system  700  using the removable storage drive  714 , interface  720 , and hard disk drive  712 , or communications interface  724 . 
     The processor device  704  may comprise one or more modules or engines configured to perform the functions of the computer system  700 . Each of the modules or engines may be implemented using hardware and, in some instances, may also utilize software, such as corresponding to program code and/or programs stored in the main memory  708  or secondary memory  710 . In such instances, program code may be compiled by the processor device  704  (e.g., by a compiling module or engine) prior to execution by the hardware of the computer system  700 . For example, the program code may be source code written in a programming language that is translated into a lower level language, such as assembly language or machine code, for execution by the processor device  704  and/or any additional hardware components of the computer system  700 . The process of compiling may include the use of lexical analysis, preprocessing, parsing, semantic analysis, syntax-directed translation, code generation, code optimization, and any other techniques that may be suitable for translation of program code into a lower level language suitable for controlling the computer system  700  to perform the functions disclosed herein. It will be apparent to persons having skill in the relevant art that such processes result in the computer system  700  being a specially configured computer system  700  uniquely programmed to perform the functions discussed above. 
     Techniques consistent with the present disclosure provide, among other features, systems and methods for analyzing user data based on social network connections. While various exemplary embodiments of the disclosed system and method have been described above it should be understood that they have been presented for purposes of example only, not limitations. It is not exhaustive and does not limit the disclosure to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practicing of the disclosure, without departing from the breadth or scope.