WO2014179690A2

WO2014179690A2 - Method and system for scoring and reporting attributes of a network-based identifier

Info

Publication number: WO2014179690A2
Application number: PCT/US2014/036581
Authority: WO
Inventors: Nguyen An NGUYEN
Original assignee: Trusting Social Co.
Priority date: 2013-05-03
Filing date: 2014-05-02
Publication date: 2014-11-06
Also published as: WO2014179690A3

Abstract

A computer-implemented method includes assigning a credibility score to a netvvork-based identifier, where the credibility score is based, at least in part, on a credibility of one or more endorsers of the network-based identifier.

Description

METHOD AND SYSTEM FOR SCORING AND REPORTING ATTRIBUTES OF

A NETWORK-BASED IDENTIFIER

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Patent Application No, 61 /819, 1 71 , filed May 3 , 2013 , and entitled Method and System for Scoring and

Reporting Attributes of User-Endorsed URL Based on Information About Endorser (s). The contents of the prior application are incorporated by reference herein,

TECHNICAL FIELD

Tbe present disclosure relates to a method and system for scoring and reporting attributes of user-endorsed network-based identifier based on information about endorser(s).

BACKGROUND

internet applications can be used to provide online transaction capabilities, such as online classifieds, auctions, and dating sites,

SUMMARY

The detail s of one or more implementations are set forth in the accompanying drawings and. the description below.

in one aspect, a computer-based method includes assigning a credibility score to a person and enabling that person to verify that he or she is associated with, one or more network-based identifiers ,

in another aspect, a computer-implemented method includes assigning a credibility score to a network-based identifier. In a typical implementation, the credibility score is based, at least in part, on a credibility of one or more endorsers of the network-based identifier.

According to some embodiments, a credibility score of a person indicates a likelihood that the person is untrustworthy. In some embodiments, the credibility score

i indicates a likelihood that the person satisfies certain criteria, such as an identity fraud risk criteria, a purchasing power criteria, a credit risk criteria, an insurance risk criteria, or a courtship criteria.

Irs some embodiments, a credibility score of a network-based identifier indicates a likelihood that the network-based identifier is associated with an untrustworthy person. n some embodiments, the credibility score indicates a likelihood that the network-based identifier is associated with a person who satisfies certain criteria, such as an identity fraud risk criteria, a purchasing power criteria, a credit risk criteria, an insurance risk criteria, or a courtship criteria, in some implementations, the credibility score of a network-based identifier indicates a likelihood that the content available at the network- based identifier is untrustworthy.

In some implementations, the method includes using an application programming interface to load the credibility score to a first web-based information resource.

In other embodiments, the method includes generating a limited-access URL, the selection of which by a selecting user, will enable the selecting user to view the credibility score for the network-based identifier only a limited number of times, and wherein, after the selecting user selects the limited-access URL the limited number of times, the subsequent selection of the limited-access URL no longer enables any user to view the credibility score.

In certain implementations, the method includes generating a limited-access URL, the selection of which by a selecting user, will enable the selecting user to view the credi bihty score for the network-based identifier only a limited number of times, and wherein, after the selecting user selects the limited-access URL the limited number of times, the subsequent selection of the limited-access URL no longer enables the selecting user to view the credibility score,

In other implementations, the method includes generating a limited-access URL, the selection of which by a selecting user, will enable the selecting user to view the credibihty score for the network-based identifier, and wherein, after a limited number of users select the limited-access URL, the subsequent selection of the limited-access URL no longer enables any user to view the credibility score. In some embodiments of the method, the endorsement is a positive endorsement or a negative endorsement.

Some embodiments of the method include enabling a user to provide system inputs, wherein the credibility score of a person or a network identifier is based, at least in pari, on the system inputs. Another implementation of the method includes accessing information from one or more data sources, wherein the credibility score is based, at least in part, on the information. Certain embodiments of the method further include accessing information from one or more data sources, transforming the information into numerical measures, wherein the credibility score is based, at least in part, on the numerical measures. In some implementations, the method includes storing the credibility score in a database.

in certain embodiments of the method, one or more endorsers are computers. Whereas i other implementations of the method, one or more endorsers are humans.

In some implementations of the method, the credibility score is represented by one or more score components.

In other embodiments, the method includes receiving scoring information from an analysis engine, wherein the credibility score can be modified based, at least in part, on the scoring information.

Many other variations of such an embodiment are possible. For example, in certain embodiments that comprise an analysis engine, the method further consists of creating input variables, wherein the analysis engine can use the input variables to create the scoring information. In another embodiment that comprises an analysis engine, the method includes creating training data, wherein the analysis engine can use the training data to create the scoring information. In one embodiment that comprises an analysis engine, the method includes performing one or more quantitative analyses, wherein the analysis engine can use the quantitative analyses to create the scoring information. In some im lementations that comprise an analysis engine, the method includes performing one or more quantitative analyses, wherein the analysis engine dynamically chooses a quantitative analysis or a combination of quantitative analyses, wherein the analysis engine can use the chosen quantitative analyses to create the scoring information. In another aspect, a computer-implemented method includes enabling a user to interact with an element at a first web-based information resource at a URL; and in response the user interacting with the element, presenting to the user specific information about the first web-based information resource, wherein the specific information includes credibility information about the first web-based information resource, and wherein the credibility information is based, at least in part, on credi i lity of one or more endorsers of the web-based information resource or the URL.

In some embodiments of the method, the element is selected from the group consisting of: a hyperlink and a form-submit-type button.

In another implementation of the method , wherein presenting to the user specific information about the first web-based information resource comprises presenting to the user a second web-based information resource that has the speciiic information about the first web-based information resource.

In other embodiments, the method includes enabling a first web server to load a credibility score of a web-based identifier to the firs web-based information resource, wherein the first web server uses an application programming interface to load the credibility score to the first web-based information resource.

Many other variations of such an embodiment are possible. For example, in certain embodiments of the method that include a first web server using an API to load the credibility score to the first web-based information resource, the first web server communicates inform tion to a second web server, including, for example, a unique identifier of the first web-based information resource, a unique identifier for a user interacting with, the first web-based information resource, and a URL.

In another aspect, a computer-implemented method includes enabling a user to select a hyperlinked domain name at a first web-based information resource; and in response to the user selecting said hyperlink, presenting to the user a second web-based information resource that, has specific information about the first web-based information resource.

In some embodiments of the method, in response to the user selecting the hyperlinked domain name, the second web-hased information resource receives http- referer information identifying the first weh-based information resource; and querying a database for information associated with the first web-based information resource to present at a first or second web-based information resource.

In another aspect, a computer-implemented method includes enabling a user to interact with an element at a first web-based information resource; and in response the user interacting with the element, presenting to the user specific information about the first web-based information resource, wherein the first web-based information resource is at an email application, chat application, SMS text application, or mobile application, wherein the specific information includes credibility informatio about the first web- based information resource, and wherein the credibility information is based, at least in pari, o credibility of one or more people who have endorsed the first web-based information resource.

In yet another aspect, a computer-implemented system includes a service server that includes a scoring engine, wherein the scoring engine calculates a credibility score of a network-based identifier, and wherein the credibility score is based, at least in part, on credibility of one or more endorsers of the network-based identifier.

In some embodiments of the system, the credibility score indicates a likelihood that a person who is associated with a network-based identifier is untrustworthy. In other implementations of the system, the credibility score indicates a likelihood that the network-based identifier includes content is untrustworthy, in certain embodiments of the system, the credibility score indicates a likelihood that a person who is associated with the network-based identifier satisfies certain criteria.

In one implementation, the system, further includes a first web server, wherein the first web server uses an application programming interface to load the credibility score to the first web-based information resource.

In another embodiment of the system, the service server is confi gured to generate a limited-access URL, the selection of which by a selecting user, will enable the selecting user to view the credibility score for network-based identifier only a limited number of times (e.g., once), and wherein, after the selecting user selects the limited-access URL the limited number of times, the subsequent selection of the limited-access URL no longer enables the viewing of the credibility score, In some implementations of the system, the service server is configured to generate a limited-access URL, the selection of which by a selecting user, will enable the selecting user to view the credibility score for the network-based identifier, and wherein, after the selecting user selects the lim ted-access URL a limited number of times, the subsequent selection of the limited-access URL no longer enables the viewing of the credibility score.

In certain embodiments of the system, the endorsement is a positive endorsement or a negative endorsement.

Still in other embodiments, the system includes access to one or more data sources. In certain implementations, the system comprises a communication unit, wherein the communication, unit controls communication between a service server and one or more data sources.

According to another aspect, a computer-implemented system includes a service server; and one or more user interfaces, coupled to the service server, wherein the service server is configured to: enable a user to interact from one of the user interfaces with an element at a first web-based information resource at a URL: and in response to the user interacting with the element, present to the user specific information about the first weh- based information resource, wherein the specific information includes credibility information about the first web-based information resource, and wherein the credibility information is based, at least in part, on credibility of one or more endorsers of the first web-based information resource.

in some embodiments of the system, the element is selected, from the group consisting of: a hyperlink and a fonn-submit-type button.

in. other implementa tions of the system, wherein presenting to the user specific information about the first, web-based information resource comprises: presenting to the user a second web-based information resource that has the specific information about the first web-based information resource.

According to one aspect, a computer-implemented system includes a service server; and one or more user interfaces, coupled to the service server, wherein the service server is configured to: enable a user to select a hyperlinked domain name at a first web- based information resource: and in response the user selecting said hyperlink, present to the user a second web-based information resource thai has specific information about the first web-based information resource.

In certain implementations, wherein, in response to the user selecting the hyperlinked domain name, the second web-based information resource receives http- referer information identifying the first web-based, information resource; and the service server queries a database for information associated with the first web-based information resource to present at the second web-based information resource.

Other features and advantages will be apparent from the following description, the accompanying drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG . 1 is a block diagram of a computer system.

FIG. 2 is a block diagram of a user interface module of FI G. 1 ,

FIG. 3 is a block diagram of a service module of FIG. 1.

FIG. 4 is a block diagram of an external data module of FIG. I ,

FIG. 5 is a block diagram of a database structure of FIG. 3.

FIG. 6 is a flowchart showing an exemplary process that may be performed by a scoring engine.

FIG. 7 is a flowchart showing an exemplary process that may be performed by an analysis endne of FIG. 3.

FIG. 7b shows a flowchart of a process that may be performed by an analysis engine.

FIG. 8 is a block diagram of a report display system.

FIG. 9 is a flow chart showing an exemplary process that may be performed by a report display system.

FIG. 10 is a screenshot of an online classified ad on a webpage.

FIG. 1 1 is a screenshot of a webpage that allows a user to endorse a URL.

FIG. 12 is an exemplary output report of a score indicating credibility or trustworthiness for a given URL,

FIG. 1 3a is a screenshot of a webpage that allows user to associate with a

Facebook account. FIG, 13b is a sereenshoi of a webpage that allows user to login to a Facebook account in order to authorize a third-party client to access his or her account.

FIG. 13c is a sereenshoi of webpage that allows user to authorize access to a third-party client to his or her Facebook account.

DETAILED DESCRIPTION

The present disclosure provides several ways to assign a credibility score to a network-based identifier, where the credibility score represents, for example, a likelihood that the network -based identifier (i) is associated with an untrustworthy person, (it) includes content that was intended to mislead a person, or (iii) is associated with a person that meets certain criteria, such as ability to purchase a given product or service.

in general, systems and methods are described herein whereby such credibility information is assigned to the network-based identifier based not only on feedback from one or more users, but also on the credibility of any users who have endorsed (e.g., provided feedback on) the network-based identifier. The term "endorsed," and similar terms, should be interpreted broadly to include having provided any feedback (either positive or negative) on the network-based identifier.

The term "network-based identifier", and similar terras, should be interpreted broadly to include, for example, website URL, a webpage URL, an account ID or user name in a web application, an account ID or user name in a mobile application, an account ID or user name in an SMS text application, an account ID or user name in a. chat application, an email address, a public key In a public-key cryptographic network, an account or payment destination identifier in a erypto-currency network or another piece of content that is accessible via the computer-based communications network.

The term "web-based information resource", and similar terms, should be interpreted broadly to include, for example, a website, a webpage, .mobile application, an SMS text application, a chat application, an email application, an image, a video or other piece of content that has access to a network.

The term "a person associated with a network -based identifier", and similar terms, should be interpreted broadly to include, for example, a person who creates, or possesses, or operates, or has access to, or has control of the network-based identifier. The term ""to associate with a network-based identifier", and similar terms, should be interpreted broadly to include, for example, to prove that a person has access to, or control of a network -based identifier, As an example, a person can associate with an email address by demonstrating that he or she has access to the emails received at the email address.

The term "information about a. network-based Identifier", and sim lar terms, should be interpreted broadly to include any type of information about a network-based identifier including, for example, information about the public and/or privat documents that are listed, recorded or referred to in connection with the network-based identifier, information about the composition of the network-based identifier such as a domain name of a URL, a domain part and/or characters compri sing the local part of an email address, or area code of a phone number.

The term '"information about a network -based identifier itself and similar terms, should be interpreted broadly to include any type of information about a network-based identifier, except for information about persons associated with the network-based identifier or endorsers of the network-based identifier.

At a very high-level, a positive endorsement from a trustworthy user might be given more weight in calculating the credibility score than an equally negative

endorsement from an untrustworthy user. Conversely, a positive endorsement from an untrustworthy user might be given less weight in calculating the credibility score than a negative endorsement from a trustworthy user. The level of trustworthiness of particular users can be determined in a number of ways, some of which are described herein in detail.

By enabling a person to associate with a network-based identifier, the credibility score described herein can provide accurate assessment of actual credibility of a network- based identifier and minimize the likelihood that an untrustworthy individual might "game the system" by providing false or misleading information.

By relating the credibility score for a network-based identifier to the credibility (or trustworthiness) of the users endorsing the network-based identifier, the credibility score described herein can provide accurate assessment of actual credibility of a network- based identifier and minimize the likelihood that an untrustworthy individual might "game the system" by providing false or misleading endorsements.

FIG, 1 illustrates an exemplary computer system for implementing various techniques (e.g., assigning a credibility score to a person, associating credibility score of a person with a network-based identifier and assigning credibility score to a network- based identifier) disclosed herein. As mentioned above, in a typical implementation, the credibility score indicates a likelihood that the network-based identifier (i) was associated with an untrustworthy person; (if) includes content that was intended to mislead another person; or (iii) was associated with a person that meets certain criteria, such as ability to purchase a given product or service: and where the credibility score is based, at least in part, on the credibility of people who have endorsed network-based identifier.

Referring to FIG. 1, the exemplary computer system includes a client interface module 1020 (e.g., a web-accessible personal computer or the like), a credibility scoring service server 1 10 coupled to the client interface module 1020 and an external data module 1030 coupled to the credibility scoring service server 1010. hi the illustrated example, the external data module 1030 has multiple Internet-based servers/data sources, including a Craigslist server 1 31, a Facebook API server 1032, a Linkedln API server 1033, other API server 1034 and a public data source 1035. In a typical implementation, the credibility scoring service server 1010 is operable to generate credibility scores for respective network-based identifier based on information from one or more client interface modules 1020 and/or one or more of the Internet-based servers. The information from the one or more Internet-based servers may include information about the users who are submitting endorsements (e.g., reviews or social profile data) associated with the network-based identifier,

The credibility scoring service 1010 can be, for example, a local intranet or web- based soiiware application. In the illustrated embodiment, the credibility scoring service 010 is web-based and accessible by an Internet user. In the illustrated implementation, the method includes inputting information .from the client interface module 1020. The method further typically includes communicating with an external data module 1030, and producing a report output 1040, External Data Module

The external data module 1030 illustrated in FIG. 1 may refer to one or more sources of data, such as websites. APIs, FTPs, local or networked databases, and other data sources. Referring now to FIG. 4, an exemplary embodiment of the external data module 1030 may include data sources like online classified websites or online auction websites. The external data module 1.030 may also include data sources from application programming interfaces (APIs), such as the Facebook Graph API. In addition, the external data module .1030 may include data sources from websites containing public records, such, as SearchSystems.net, the Maricopa County Recorder's database, or the U.S. SEC Electronic Data Gathering, Analysis and Retrieval (EDGAR) system. The external data module 1030 may be coupled to the credibility scoring service 1011} through a variety of means, including a local area network (LAN); a wide area network (WAN); an Intranet; or a network of networks, such as the Internet.

Client interface Module

Turning now to FIG. 2, one embodiment of a client interface module 1 20 is provided. The client interface module 1020 consists of a user 2010 interacting with an interface 2020 to input a variety of system inputs 2025, A user 2010 may consist of a person or a computer, such as a web server. In one embodiment, where the user 2010 is a person, the interface 2020 can. consist of a computer terminal with a graphical user interface, such as a web-application form. In. another embodiment, where the user 2010 is a computer (e.g., a web server, general purpose computers, or mobile computing devices), the interface 2020 can include an. application programming interface (API).

The system inputs 2025 vary depending on the embodiment. In one embodiment, the system inputs 2025 may include an input uniform resource locator (URL) (the "input

URL") 2040; signals indicating an up-vote or down-vote of content on a website 205Θ; credentials to online accounts 2030, such as social networking accounts (e.g., Facebook.

Linkedin, Twitter) or bank accounts; images 2060: text or numeric data 2070, such as passport or license numbers, bank account numbers, or social security numbers; among other system inputs 2080. An example of other system inputs 2080 can include an evaluation of the content or the endorser, such as "I pledge that this product is genuine

I t and authentic." In another embodiment, an endorser oilier than the user 2010 may provide an evaluation of the user 2010 as follows: "Joe Smith is a trustworthy person. Buy with confidence from Joe Smith." In another embodiment, other system inputs 2080 can include comments about content on a webpage, For example, "The used iPhone listed in this ad is genuine."

In the illustrated implementation, the person user 2Θ10 interacts with a computer terminal that provides a graphical user interface 2020 to submit system inputs 2025 consisting of an Input URL and credentials to the user's 2010 social networking accounts.

In certain embodiments, a user 2010 can optionally endorse limited sections of content on a webpage referenced by a URL. For example, a user 2010 can add a text comment to an article or a blog on a webpage that contains many other text comments. Typically, the user 2010 can selectively endorse their own comment (or any other comment) on the webpage, such that a credibility score is narrowly associated with the combination of the URL and a comment identified by the user 2010. Such an

embodiment provides an interface allowing the user 201 § to submit an input URL 2040 and the identification of the comment.

In another embodiment, a user 21)10 may endorse an email address (e.g..

someone@gmail.com) rather than a URL, thereby associating a credibility score to that email address, in such, an embodiment, a person can query a database 3010 for the emai l address to view a credibility score associated with that email address. Such an

embodiment typically includes an interface allowing the user 21)10 to submit an email address as a system input 2025.

In another embodiment, a user 2010 may endorse a public key In a public-key cryptographic network rather than a URL, thereby associating a credibility score to that public key. in such an embodiment, a person can query a database 3010 for the public key and the network name to view a credibility score associated with that public key . Such an embodiment typically includes an interface allowing the user 2010 to submit an public key as a system input 2025,

In another embodiment, a user 2010 may endorse a payment destination identifier in a crypto-eurrency network rattier than a URL. (e.g., bitcoin:

3J98tlWpEZ73CNmQviecrny), thereby associating a credibility score to that payment destination identifier, In such an embodiment, a person can query a database 30J .0 for the payment destination identifier and the name of the crypio-currency network to view a credibility score associated with that payment destination identifier. Such an embodiment typically includes an interface allowing the user 2010 to submit a payment destination identifier as a. system input 2Θ25.

FIG, 2a. relate to an exemplary computer-based method that includes enabling a user 2010 to verify that he or she is associated with a network-based identifier. In such an embodiment, the user may associate with an email address by first inputting the email address via the first interface 2020. The user then receives a verification email from the service 1Θ10 that includes a verification URL. The user may select the verification URL on the verification email. Based on whether the verification response from the user meets pre-determined criteria for verification purposes, the system may verify that the user has access to or control of the network-based identifier.

FIG. 2b illustrates an exemplary computer-based method that includes enabling a user 2010 to verify that he or she is associated with a phone number (i.e., a network- based identifier) by first inputting the phone number via a first interface 2020 (e.g., by viewing a webpage). The user then receives a verification request from the servic 1010 via a second interface 2221 (e.g., a phone associated with the phone number), such as a text message that includes a randomly-generated verification code. The user may send a verification response to the verification request. Sending the verification response may involve, for example, inputting the randomly-generated verification code to the first interface. Based on whether the verification response from the user meets pre-determined criteria for verification purposes, the system may verify that the user has access to or control of the network-based identifier.

FIG. 2e illustrates an exemplary computer-based method thai includes enabling a user 2010 to verify that he or she is associated with a network-based identifier (e.g., an account ID or a user name associated with a particular website, such as Facebook or the like), in such an embodiment, the user may associate with an account ID or a user name on a website (e.g., the Facebook website) by first inputting the network-based identifier via a first interface 2020 (e.g., a website). The user then receives a verification request from the service 1010 via a second interface 2321, such as a pop-up window that requires authorization of the service .1.010 as a third-party client by an authorizing server associated with the website. The user may send a verification response to the request. The response may include authorizing the service 10.1.0 to access his or her account, information as a third-party client. Based on whether the verification response from the user meets pre-deterrnined criteria for verification purposes, the system may verify that the user has access to or control of the network-based identifier.

FIG. 2d il lustrates an exemplary computer-based method that includes enabling a user 2Θ1Θ to verify that he or she is associated with a network-based identifier (e.g., a public key in a public-key cryptographic network). In such an embodiment, the user may- associate with the public key in the public-ke cryptographic network by first inputting the network-based identifier via a first interface 2Θ20 (e.g., a website or other type of interface). The user then receives a verification request from the service 1010 via a second interface 2421, such as a message encrypted using the public key inputted by the user. The user may respond to the request by decrypting the message encrypted using the public key previously inputted by the user, re-encrypting the message using a public key provided by the service, and sending the message to the service 1010. Based on whether the verification response from the user meets pre-deterrnined criteria for verification purposes, the system may verify that the user has access to or control of the network- based identifier.

FIG. 2e illustrates an exemplary computer-based method that includes enabling a user 2010 to verify that he or she is associated with a network-based identifier (e.g., a payment destination identifier in a crypto-currency network). In. such an embodiment, the user may associate with the payment destination identifier in a crypto-currency network by first inputting the payment destination identifier via a first interface 2020 (e.g., a webpage), The user then receives a verification request from the service 1010 via the second interface 2521, such as a payment request of one or more randomly-generated amounts. The user may respond to the request by sending the requested payment amount(s) to a payment destination identifier provided by the service .1.010. Based on whether the verification response from the user meets pre-deterrnined criteria for verification purposes, the system may verify that the user has access to or control of the network-based i den tifier,

FIG, 2f illustrates an exemplary computer-based method that includes enabling a user 201 to verify that he or she is associated with a network-based identifier (e.g., a web page URL). In such an embodiment, the user may associate with a web page by first inputting the web page URL via the first interface 2020 (e.g., a webpage). The user then receives a verification request from the service 1010 via the second interface 2621 , such as a randomly generated verification code (e.g., at the first interface or in an email). The user may respond to the request by inserting the verification code to the web page. The service 1010 makes a request to the web page URL to verify that, the verification code from the web page matches the verification code sent to the user 2010. Based on whether the verification response from the user meets pre-determined criteria for verification purposes, the system may verify that the user has access to or control of the network- based identifier.

Credibility Scoring Service

Referring now to FIG. 3, one embodiment of a credibility scoring service 1010 is provided, The credibility scoring service 1010 includes a database 3010, a

communication uni 3020, a scoring engine 3030, and an analysis engine 3050. In typical embodiments, such elements interact with system inputs 2025 and external data module 1030. As one skilled in the art would understand in light of the present description, the credibility scoring service 1010 in FIG. 3 may include other components in addition to or instead of the enumerated components of FIG, 3. The components 3010-3050 of FIG. 3 are not intended as an exhaustive listing, but rather as an exemplary set of components for descriptive and presentation puiposes.

Database

in a typical embodiment, the database 301.0 stores various information pertaining to the system inputs 2025, such as an input URL 2040 and a user's 2010 social profile credentials 2030, as well as information from the external data, module 1030, which may include information from networks like Facebook, Linkedln, Twitter, and so on. In the illustrated embodiment of FIG, 3, the database 301 also includes the report output 1040. in a typical embodiment, the database 3010 associates the system inputs 2Θ25, the information from the external data module 1039, and the report output 1040, using one or more unique identifiers. By way of example, FIG. 5 illustrates a data structure 5000 for storing information in the database 3010. The data structure 5000 includes one or more elements, such as a User table 5100. Account Access table 5200, Endorsement table 5300, and a URL table 5400. In the illustrated embodiment of FIG. 5, the User table 5 00 stores information about a given user 2010, including a unique identifier and a output report (referred to here as a "Credibility Score"), and myriad other inputs. The Account Access table 5200 includes information about physical or online accounts for which the user 2010 may provide information, such as social networking accounts (e.g., Facebook) and bank accounts (e.g., bank account numbers, routing numbers, or online access credentials). The Endorsement table 5300 stores information that associates information from the User table 5100 and information from the URL table 5400. The URL table 5400 stores information about a URL 5410 (which may be an Input URL 2040 provided by a user 2010 or a system generated URL for single-use authentication), a score 5420, and various components that comprise the score 5430-5450. The score 5420 can refer to a result, which in some embodiments are expressed as text, numbers, and/or images.

In the illustrated embodiment, the score 5420 refers to a result, such as an output report 1040 conveying the credibility or trustworthiness of a URL 5410. expressed as text, such as "A" for an excellent credibility rating or "F" for a poor credibi lity rating. In other embodiments, the score 5420 can refer to a result, such as an output report 1 40 that expresses the credibility of a URL 54.10, expressed in a numerical form, such as "200" for an excellent credibility rating or "50" for a poor credibility rating. A credibility rating associated to a particular number or range of number may change over time, and that the numbers may be scaled and normalized to account for such a dynamic situation.

In yet another embodiment, the score 5420 can refer to a result, such as an output report 1040 conveying the purchasing power credibility (e.g., the ability to purchase, generally measured by income) of a URL 5410, expressed as text, such that "A" signifies a high ability or propensity to purchase whereas "F" signifies a low ability or propensity to purchase. For example, it is contemplated that the credibility scoring service 1010 may crawl publicly available websites, such as Linkedln URLs, analyze content and social network data (such, as a URL belonging to Linkedln user (a "profile URL")), and generate a purchasing power credibility score for each said profile URL.

Communication Unit

Referring back to FIG. 3, a communication unit 3020 is provided in the illustrated embodiment. The communication unit 3020 can read information stored in the database 3Θ1Θ, communicate with an external data module 1030, and write information in die database 3010. In certain embodiments, the communication unit 3Θ2Θ can read user 2010 information, such as social networking account credentials, from the database 3010. ^'The communication unit 3020 also can communicate with the external data, module 1030 using application programming interfaces (APIs), such as Faeebook's Graph API In such an embodiment, the communication unit 3020 provides information to the Graph API, such as a user's 2010 credentials (e.g., an "Access Token"), thereby allowing the communication unit 3020 to read information about the user 2010 from the Graph API. Such information about the user 2010 can be written to the database 3010.

The communication unit 3020 can include a computer or web

server. Furthermore, communications between the communication unit 3020 and the database 301Θ or the communication unit 3020 and the external data module 1030 can be accomplished through a variety of means, such as wireless transmission, a local area network (LAN); a wide area network (WAN); a Intranet; or a network of networks, such as the Internet.

Scoring Engine

A scoring engine 3030 is provided in FIG. 3. To illustrate one such embodiment of the scoring engine 3030, FIG, 6 depicts an exemplary process that may be performed by the scoring engine 3030, which includes the step 6010 of obtaining information from the database 3010, In. step 6020, the information obtained from step 6010 can be transformed, into one or more numerical measures using mathematical functions. A scoring function can be applied in step 6030 to the transformed numerical information obtained from step 6020, The result of the scoring function in step 6Θ30 can be stored in a database 3010.

Data Transformation

By way of example, one embodiment may transform information obtained from step 6010 - such, as a user's 2010 social data from Facebook - by taking the natural logarithms of certain variables, suc as (i) the number of friends, (ii) the number of active friends (e.g., those available for chat), and (iii) the number of people with whom a user 2010 has been friends for more than four years. Certain embodiments could use different variables to produce a result; such as an output report 1040,

Moreover, other transformations may include normalizing and scaling numerical information. Other embodiments can include transformations thai count the frequency of characters in text, information to create a numerical representation of the text

information. Still other transformations may include one or more image processing techniques that create numerical representations of the image information. For example, one can create an indicator that a person^' s face in one image is of the same person in another image, using a variety of facial recognition methods such as Principal

Component Analysis, Kernel Principal Component Analysis, linear Discriminant Analysis for feature extraction and State Vector Machine, Random Forests or Artificial Neural Network for classification, among others. Such an. indicator can be evidence that the user 2010 is credible or trustworthy by verifying that said user's face appears in multiple online accounts.

Scoring FmietioK

As noted above, the illustrated embodiment in FIG. 6 includes a scoring function in step 603Θ. In some implementations, the scoring function 6030 is the mathematical sum of n- variables (e.g., each of the transformed numerical outputs from step 6020). Optionally, each variable has a coefficient to provide weight to each variable. For example:

In this embodiment, the numerical score resulting f om, this sum equation is stored in a database 3Θ10, Coefficient weight can be assigned in various ways, such as human- supplied values based on initial estimates or analysis (e.g., regression analyses, like those identified below), or using an analysis engine 305©.

Analysis Engm.a

An analysis engine 3050 is shown in FIG, 3. In a typical implementation, the analysis engine 3050 updates the scoring function of the scoring engine 3030 on a periodic basis. For example, an analysis engine 3050 may execute instructions to create an updated scoring function based on new (or old) information available to the overall credibility scoring service 1010. Accordingly, the present disclosure contemplates a system and method that learns from available data and automatically updates its scoring capabilities.

To illustrate one exemplary embodiment, FIG. 7 shows an exemplary process that may be performed by the analysis engine 3050. The exemplary process includes a step 701 of obtaining raw attributes from a database 3010, In one embodiment, the raw attributes refer to variables that have not been transformed. Step 7020 depicts creating input variables from die raw attributes obtained in step 7010. such as without limitation, combining two raw attributes to express a ratio. For example, one raw attribute may be die number of friends a user 2010 has in the Facebook social network, and a second raw attribute may be the number of active friends (e.g., those available for chat). In this example, step 7020 creates an input variable by expressing the two raw attributes as a ratio, such as the number of active friends divided by the number of friends. There are myriad ways to create input variables in step 7020 by using combinations of variables.

Step 7Θ30 includes creating a training dataset. including one or more input variables and target variables. In this embodiment, the input variables are those variables created in step 702Θ, whereas a target variable refers to an outcome of interest. For example, a target variable can be an indicator of whether a Facebook account has been identified as genuine (I.e., belonging to a real genuine person, not an imposter or fake persona), in which case the target variable may be a binary number. The training dataset can be divided (manually or automatically) to prevent overtraining (i.e., overfitting) the credibility scoring engine 3030.

The illustrated process includes the step 7040 of applying quantitative analysis (e.g., regression analysis) to the training datasei obtained from step 7029. The quantitative analysis of step 7040 varies among embodiments. Generally speaking, the term quantitative analysis refers to techniques for estimating the relationships among variables. Such quantitative analysis techniques can include, for example, parametric regression, non-parametric regression, supervised learning, statistical classification, unsupervised learning, association rule learning, hierarchical clustering, random forest, bagged trees, neural networks, reinforcement learning, among others and combinations thereof,

In the illustrated process, step 7040 includes applying a linear regression analysis, In the present embodiment, the linear regression analysis will produce a polynomial equation, where each input variable is multiplied by a coefficient. Step 7050 includes identifying the coefficients from step 704Θ and creating another scoring function, which may be different than the scoring function in step 6030, For example, the scoring function from step 6030 had coefficients a_n with particular values, whereas the scoring function from step 7050 may generate different values for coefficients #„ based on the analysis in step 7040. The scoring function from step 7050 is provided to the scoring engine 3030 to complement or replace the scoring function applied in step 6030, in this way, the scoring engine 3030 can adapt to additional information.

The scoring function provided in step 7050 can be a form that is entirely different than the scoring function in step 6030, For example, the scoring function from step 6030 may be of the form

whereas the scoring function from step 7050 may be of another form

FIG, 7b illustrates an embodiment similar to, but different than, the one described in FIG. 7, In this embodiment, an analysis engine 3050 can include one or more processes 711 0-7300 to predict certain attributes thai can be useful in generating a.

scoring function. In one such embodiment, each process 100 and 7200 can include the steps outlined above 7010-7060 to create a scoring function that is applied to the raw data attributes In order to generate predicted attributes, which are stored in a database

301 . Then a separate process 7300 can obtain raw attributes and the predicted attributes generated in steps 7060 of each process 7100 and 7200 from the database 3010.

Afterward, the process 7300 can follow steps 7020-7060 as described above to create a training dataset apply a regression analysis, estimate parameters, and create a revised scoring function that will be supplied to the scoring engine 3030.

Score or Output Report

Referring back to FIG. 3, a score (or output report) 1040 is provided via any number of human- or machine-readable formats, for example, on computer terminals, displays, mobile computing devices, and printouts, among others. Generally speaking, the score 1040 refers to a result, such as a report conveying the credibility or trustworthiness of a network-based identifier, where the credibility score indicates a likelihood that the network-based identifier (i) was associated with an untrustworthy person; (ii) Includes content that was intended to mislead a person; or (iii) was associated with a person that meets certain criteria, such as ability to purchase a given product or service; where the credibility score is based, at least m part, on the credibility of people who have endorsed the network-based identifier.

In other embodiments, the score 1040 can refer to a result, such as a report conveying the purchasing power credibility of a URL, expressed as text, such as "A" for aii excellent rating or "F" for a poor rating.

In some embodiments the score 1040 is associated to an input URL 2040 and a user 2010. In other embodiments the score 1040 Is associated to a random, limited- access, and internally generated URL (e.g., wwwTro3tingSocial.com/Xj23S) that is recorded in a database 3010 and is subsequently destroyed after a limited number of times the website renders that internally generated URL. This alternative embodiment

2 \ allows a user 2010 to share their credibility score with a stranger without fear that the stranger will re-transmit the URL, in pretending to be the user 2010 by sharing it with others.

In still other embodiments the score 1040 is associated to a random, limited- access, and internally generated URL (e.g., www.TrustingSoc3al.com/Yg75A), that limits access to the URL to one or mote viewers. For example, the website of the internally generated URL, TrustingSocial.com in this embodiment, can insert a web cookie on a viewer (i.e., sending a small piece of data sent from a website and stored in a user's web browser while a user is browsing a website). In this embodiment, the viewing of the URL may be limited to a number of web cookies, and thus to a limited number of viewers. It should be understood that a viewer can be a person, or a computer. This alternative embodiment allows a user 2010 to share their credibility score with a limited number of viewers --^■ without limiting the number of times they can view the URL - while reducing fears that a viewer will re-transmit the URL in pretending to be the user 2010 by sharing it with others.

In certain embodiments, a score 1 40 may include component scores, such as Vers fi ability. Connectivity, and Maturity for a user 2010, where "Verifiability" refers to how well a user 2910 can pass certain verification measures, such as claiming a .edu email address; "Connectivity" refers to how closely a user's 2010 social network represents a typical pattern of a. group of other users; and "Maturity" refers to how long a user 201 has been using online social media. Such component scores are obtained or calculated from a scoring engine 3030.

Accessing the Output Repmify)

Web browsers (e.g., Internet Explorer, Chrome. Safari. Firefox and Opera) generally identify and transmit an "HTTP referer" field that identifies the URL of a first website in the header information sent to a server of a second website when a browser makes a web-based resource request (the "Referring URL").

FIG. 8 illustrates an exemplary method of a computer-based method that includes enabling a user 8010 to select a hyperlink 8020 at a first web-based information resource 8030; and in response the user selecting the hyperlink 8020, presenting to the user a second web-based, information resource 8Θ50 thai has specific information about the first web-based information resource 8960, where the specific information includes credibility information about the first web-based information resource 8970 (and where the credibility information may be based, for example, at least in part, on the credibility of people who have endorsed the first web-based information resource.).

In one embodiment of FIG, 8, a user (e.g., a potential buyer) 801 views a webpage listing from an online classifieds website (i.e., the first web-based information resource) 8039, which includes text and a hyperlink 8020 like this; "This listing has been authenticated by www .Trusti ngSoei al . co m" Clicking on the hyperlink 8020 enables a browser 8980 to send an HTTP request 8990 - where such HTTP request includes, among other things, a reierrer URL 8095 field - to a second website server 8.190, The second website server 8.100 queries a URL database 8110 (such as the database 3010 illustrated in FIG. 3). Upon finding a URL in the URL database 8110 that matches the referrer URL 8095, a second webpage 8059 is rendered that contains specific information about the first webpage 8030, where such specific information can include credibility or trustworthiness information about the first webpage 893Θ. In this example, the credibility information about the first webpage 8039 may be based, at least in part, on the URL score 1040 illustrated in FIGS. 1 and 3.

Referring now to the flowchart in FIG. 9. in step 9ΘΘ0. the user is enabled to click on a. hyperlink on the first website, such as www.1 rust; gS pel a I .com . in step 9019, a user clicking on a hyperlink in a first webpage is redirected to a second webpage, whereupon the browser sends a request to the second website's server, including the Referring URL variable.

In step 9020, the second website server reads the Referring URL and, optionally, stores the Referring URL and other header information in a database, in step 9030, a query is made on a database, such as database 3910 identified in FIG. 3, where a query parameter includes the Referring URL, The query in step 9030 may return a query result indicating that the Referring URL exists in the database 39.10 and has certain associated credibility information, such as a URL score 1049, about the first webpage. In step 9Θ4Θ a second webpage is rendered, containing specific information associated with the first webpage, such as credibility or trustworthiness information about the first vvebpage based at least in pari, on the URL score 11)40. in step 9050, a browser displays the information rendered by the second website server in step 9040.

In another embodiment, similar to the one illustrated in FIG. 8, a computer-based method includes enabling a user 80 J 0 to select a hyperlink 8020 at a first web-based Information resourc 8030; and in response the user selecting the hyperlink 8020, presenting to the user specific information (such as a credibility rating) about the first webpage 8030 within the first webpage 803Θ by using JavaScript code that instructs a browser to send an AJAX (Asynchronous JavaScript and XML) request, to the second website's server 8100, receiving the specific information about the first webpage 8030 from the second website's server 8100, and rendering the specific information on the first webpage 8030 in image, video, text or HTML format.

In yet another embodiment, similar to the one illustrated in FIG. 8, a computer- based method includes loading a first web-based information resource 8030; triggering a JavaScript code that instructs a browser to send an AJAX request to the second website's server 8100; receiving specific information about the first webpage 8Θ30 from the second website's server 8100; and rendering specific information (such as a credibility rating) about the first webpage in image, video, text or HTML format

An Exemplary Urn Case

In one exemplary embodiment, a person 2030 wants to sell a used iPhone

(hereafter the "Seller") on a website that offers online classifieds, like

Craigslisi.org. Because there are so many items for sale on the online classifieds website, the Seller 2010 wants to offer potential buyers peace of mind that the Seller 2010 is not a fraudster. The Seller 2010 engages the TrustingSoeial.com system, which is one embodiment.

The Seller 2010 suhmits user- generated content to Craigshst.org to create the online classified advertisement or listing at a first webpage 8030. For example, FIG. 10 depicts a typical online classified listing at the first webpage 8030, where the Seller 2010 describes her item for sale, in this case a "used iPhone", includes the pricing information, and how a potential buyer can contact the Seller 2010. In addition to such common elements, the Seller includes the following text and hyperlink 30010, "Buy with confidence. This ad has been authenticated by w w . Tr u st ; n S oc i al .com." After submitting this user-generated content, Craigslist.org will send the Seller 2010 an email confirming that the information was received in addition to providing a URL for the Seller's 20 0 online classified ad 10020 (the "Ad URL").

Now the Seller 2010 can endorse this ad (i.e., associate Seller's 2010 credibility score to this particular URL). To do so, the Seller 2010 can navigate to

^ί._ΐ^Ι^ which provides a client interface 2020 depicted in FIG. 1 1 . In this embodiment, the client interface 2020 includes an input form 1.1010; an

authentication button 11020, such as "Authenticate this link with Facebook"; a search button 11030, which can be used to query the database for a. given URL; and a hyperlink to create a "Single authentication with Facebook" 1.1040, which can be used to generate a credibility scored URL- that is valid for a limited number of page views or time. The Seller inserts the Ad URL 10020 (e.g., the Input URL 2040) in the input form 11010 and clicks on the authentication button 11020. The TrusiingSociai web application prompts the Seller 2010 to grant TrusiingSociai access to the Seller's 2010 Facebook account information (e.g. allow access to online accounts 2030).

After the Seller 2010 submits the Ad URL 10020 and provides access to her Facebook account, the TrusiingSociai web application running on an Amazon EC2 server (e.g., a credibility scoring service 1010 implemented on a second website's server 8100 referred to hereafter as "TS application") executes certain instructions to calculate a credibility score 1040 for the Ad URL 10020. First, the TS application will cause a web server running Ruby on Rails to communicate HTTP requests and responses (e.g.. a communication unit 3020) with Facebook' s API server 1032 to pull data from the Seller's 2010 Facebook account (e.g., an external data module 1030), including the number of Facebook friends associated to Seller 2010, how many active friends Seller 2010 has available for chat on Facebook, and many other attributes. The web server will communicate with a PostgreSQL database (e.g., a database 3010) to store the information obtained from the Facebook API Server 1032.

Second, the TS application will cause the web server to access data from the PostgreSQL database 6010, transform information into numerical measures 6920, apply a scoring function to the numerical measures 6030. and save the score to the PostgreSQL database 6040. In this embodiment, the web server transforms non-numeric information into mimerical measures, such as generating a binar measurement if an email associated with the Seller's 2010 Facebook account is an educational email account (e.g., ja 'C ⁾. harvard.edu) ("XI"). The web server also applies other mathematical functions to the data, such as taking the natural logarithm of the number of Facebook friends in Seller's 2010 account ("X2"), and/or finding the percentage of Facebook friends who are active on Facebook' s chat application ("X3"). The web server then applies a scoring function to the aforementioned mimerical measures, like this:

Score 10*(X1) + 0.5*(X2) + 3*(X3)

The web server then stores this score in the PostgreSQL database, relating the score to the Seller 2010 and the Ad URL 10020 (e.g., using unique identifiers and data tables in a data structure 5100-5400 as depicted in FIG. 5). While the web server calculates the score (e.g., a U L score 1040), the TrastingSocial .com website can display a process indicator, such as a spinner or loading bar.

Turning now to FIG. 12, after the web server completes its calculations, the TrastingSocial website renders a second webpage 8050 that can display a score 12010 indicating credibility or trustworthiness of the Ad URL 10020 (e.g., specific information about the first webpage 8060, including credibility information about the first webpage 8070), based at least in part on the Seller's 2010 endorsement of the Ad URL 10020.

Later, a potential buyer browsing various Craigslisi.org classified ads navigates to the Seller's 2010 ad illustrated in FIG. 10, where the Ad URL 10020 is

http:/ sample,craigslist.org/rnnh/mob/3767357011.html. The potential buyer sees the TrustingSocial.com hyperlink 10010 and clicks on it (e.g., step 5⁾00( ). The potential buyer's Internet browser sends an HTTP request to the TrustingSociai web server, including the Ad URL 10020 as the header HTTP referer field (e.g., step 9010), ^'Five TrustingSociai web server reads the Ad URL 10020 (e.g., step 9020) and queries the PostgreSQL database for a matching URL. (e.g., step 9030). In this example, the Seller 20.10 has endorsed the Ad URL 10020, and the web server will accordingly find a match in the PostgreSQL database. The TrustingSociai web server renders a second webpage 8050 containing the information associated to the matched Ad URL 10020, such as the previously calculated credibility score 12010 for the Ad URL 10020. The potential buyer's internet browser displays the information rendered by the TrustingSocial weh server, Accordingly, the potential buyer will see the credibility report (including a trustworthiness or credibility score 12010) illustrated in FIG. 1.2 for the Seller's 201Θ Ad URL 10020.

Alternative Embodiments

Many other embodiments arc possible. In some embodiments various system elements or steps may be omitted or rearranged.

In one embodiment, similar to the one illustrated in FIG. I, the system may omit a client interface 1020. For example, instead of receiving system inputs 2025 from a client interface 1020, such an embodiment crawls a predetermined or open set of websites and calculates credibility scores for each website's URLs. For example, a credibility scoring service 1010 may periodically crawl information on the internet (e.g., 1030), such as Linkedln profiles, and calculate credibility scores 1040 for URLs that are associated to member profiles.

In certain embodiments, a computer (e.g., a web server) can endorse a URL, where the credibility score 1040 for a given URL is based on the trustworthiness of the computer, rather than a person. For example, in such an embodiment the computer systems may be a social network "hot" operating on a web server.

In another embodiment similar to FIG. 1, the credibility scoring service 1010 does not communicate with an external data module 1030, Instead, the credibility scoring service .1.010 calculates credibility scores for an internal non-networked data set (such as information submitted via a memory device or loaded directly into a database 3010).

Referring now to FIG. 3, certain embodiments may omit a communication unit 3020. In such embodiments, a scoring engine 3030 can communicate directly with an external data module .1.030. In other embodiments, neither the communication unit 3020 nor the external data module 1.030 is necessary as described in the preceding paragraph.

In some embodiments, a database 3010 can be omitted from the credibility scoring service 1010. in such an embodiment, the information from an external data module 1030 is stored in RAM and/or directly coupled to a scoring engine 3030.

In other embodiments, an analysis engine 3050 can be omitted from the credibility scoring, service 1010. In this embodiment, the scoring engine 3030 may use a

2? static scoring function or a system administrator may manually modify the scoring function from time to time,

in still another embodiment, an external data module 1030, similar to the one depicted in FIG. 4, can omit requests to information sources, in this alternative embodiment, the information sources may push information to a credibility scoring service 1010 without a call request, For example, an RSS feed may be created that pushes information to the credibility scoring service 101Θ.

Referring now to FIG. 5, many other data structures are possible to accomplish the alternative embodiments described herein. For example, in one alternative embodiment that does not include a client interface 2020, the data structure in FIG. 5 may omit a User table 5100 and/or an Account Access table 5200.

in other embodiments, certain steps depicted in FIG. 6 may be re-ordered or omitted. For example, step 6010 can be omitted when the credibility scoring service receives pushed information rather than pulling data from an external data module, in another embodiment, step 6020 may be omitted, as it is not necessary to transform data into numerical measures. In such an. embodiment, a scoring engine can use data that is already in numerical form, such as the number of Facebook friends a user 2010, and the like. In. yet a different embodiment, step 6030 can calculate a score based on non- numerical attributes, such as matched text. For instance, a user's 2010 web-based resource content (e.g., online classified advertisement text) may score a low credibility rating if the content matches certain text phrases, such as "wire the money in advance" among others. In another embodiment, step 604 can be omitted such that the score is not saved in a database 3010, In such an embodiment, for example, the score obtained from the scoring function can be stored in RAM, sent to another system or scoring engine, or displayed immediately on an Internet browser or other suitable display.

Still other embodiments are possible by modifying the steps illustrated in FIG, 7. For example, step 7020 can be omitted, such that an analysis engine 3050 can create a scoring function from the raw attributes in a database. For example, instead of creating input variables, such as the ratio of two raw at tributes (e.g., the ratio of the number of a user's Facebook. friends to the number of the number of a user's Facebook friends available for chat), the analysis engine 3050 can create a scoring function based on the raw attributes themselves. In another embodiment, step 7030 can be omitted, because supplying training data is not necessary for certain types of analysis steps 70411, such as calculating the mean or standard deviation in a statistical distribution analysis.

As noted herein, certain embodiments contemplate modifying quantitative analysis step 7040 to perform a variety of different analyses, such as parametric regression, non-parametric regression, supervised learning, and many others. Some embodiments may perform one of these various analysis types or multiple such analyses. Another embodiment of step 7040 contemplates dynamically choosing analysis t pe(s) that create the most accurate model for a given data set.

In another embodiment, similar to the one illustrated in FIG. 8, a computer-based method includes enabling a user 8010 to select a hyperlink 8020 in a text message (e.g., SMS or MMS) or within a texting, chatting or email application 8030, and in response to the user selecting the hyperlink 8920, presenting to the user specific information (such as a credibility rating) about the URL included in the hyperlink 8020 in a display 8050 (such as an Internet browser window, mobile device, or other display devices). In this embodiment, the URL can be randomly and internally generated as described herein.

In one embodiment, a first website can communicate with a second website's server to endorse URLs on behalf of the first website's users. In such, an embodiment, the first website can send requests to a second website's API server, containing a unique identifier of the first website (e.g., a domain name or unique token), a unique identifier of a first website's user (such as email or unique user number), and a URL- (the "provided URL"). The second website's API server queries a database 3010 to find the first website's user, and upon finding a match, can generate a URL score 1040 (e.g., a credibility score indicating trustworthiness) for the provided URL, and communicate said URL score to the first website. If the second website's API server cannot find a user match in a database 3010, then the second website's server can enable the first website's user to endorse the provided URL or instruct the first website's server to do so.

For example, the second website's server can communicate information to the first website's server, such as a button image and other HTML elements that allows a. first website's user to provide access to their online accounts. In response to a first website's user clicking on said button, the first website's server can send an AJAX request to the second website's server, including a provided URL and the first website's user^'s unique identifying information (such as an email address). The second website's server then can generate a URL score 40 and communicate it to the first website,

In one embodiment, a user 201( may endorse an email address (the "Email Sender") that can send an email message to another person (the "Email Recipient^''). For example, the Email Sender 2010 coukl visit a website, such as TrustmgSoeial.com, and request a random, internally generated URL (the "generated URL") that can be inserted into an email message. The Email Recipient can view the email message in an email application (e.g., Mozi!la Thunderbird, Microsoft Outlook, and the like), which includes the generated URL. The Email Recipient can click on the generated URL in order to view the Email Sender's credibility score on the TrustingSocial .com website.

In certain embodiments, a user may interact with web-based information resources, such as text, images, buttons, hyperlinks, and the like, at a mobile web application. For example, the mobile application can present a profile picture of a member of the mobile application. Upon clicking the profile pict ure, the mobile application can display a credibility score of the member, where the credibility score is based, at least in pari, on the credibility of the member and credibility of other members who endorsed the member.

FIG. 13a, FIG. 1 3b, and FIG, 13c present a sequence of sereenshots that enable a user to associate with a social network account (e.g., Facebook) from a first website inipiementing the method described in this disclosure. FIG, 13a presents a screenshot of a webpage that enables the user to select which social account he or she wants to associate with. The user may select the button 130.1.0 on the webpage, after which the user is redirected to a new webpage similar to FIG. 13b. The webpage enables the user to sign in to his or her Facebook account. After the user signs in, an authorization webpage similar to FIG. 13c is presented to him by the Facebook website. The user may choose to authorize access to his or her account by selecting the button 13310. after which the user is redirected to the first website. This completes the process of associating the user with a Facebook account.

Other implementations are within the scope of the claims.

Claims

What is claimed is:

1. A computer-based method comprising:

enabling each respective one of a plurality of individuals to provide access to information relevant to credibility of that individual;

assigning a credibility score to one or more of the individuals based on the accessed informs lion;

enabling one or more of the individuals to associate with one or more network- based identifiers;

receiving, at a computer-based server, a message that identifies one of the network-based identifiers; and

in response to receiving the message, returning via a computer- based communications network, the credibility score for one or more of the individuals associated with the network-based identifier in the message.

2. The computer-based method of claim 1, wherein the credibility score returned in response to the received message is adapted to be rendered at a computer-based user- interface without revealing an identity of the one or more indi viduals whose credibility score is being returned,

3. The computer-based method of claim 1 , further comprising

calculating the credibility score for each respective one of the individuals, wherein calculating the credibility score is based, at least in part, on online social media data associated with each respective one of the individuals from one or more social media, sources, or other informatio sources, on the Internet.

4. The computer-based method of claim 3, wherein enabling each respective one of a plurality of indi viduals to provide access to information relevant to credibility of thai individual comprises:

enabling each respective one of the individuals to authorize access to at least some of the online social media data.

5. The computer-based method of claim 3, wherein calculating the credibility score for each respective one of the individuals is based, at least in part, on one or more endorsements of the individual from one or more other individuals,

6. The computer-based method of claim 5, wherein calculating the credibility score for each respective one of the individuals is based, at least in part, on one or more credibility scores associated with each respective one or more other individuals who have endorsed that individual.

7. The computer-based method of claim 1, wherein each credibility score indicates a likelihood that an individual who is associated with a network-based identifier satisfies criteria selected from the group consisting of; an identity-fraud risk criteria, a purchasing power criteria, a credit risk criteria, an insurance risk criteria, or a courtship criteria.

8. The computer-based method of claim 1 , wherein each one of the network-based identifiers is or is available at:

a website URL, a webpage URL, an account I D or user name in a. web

application, an account ID or user name in a mobile application, an account ID or user name in an SMS text application, an account ID or user name in a chat application, an email address, a public key in a public-key cryptographic network, an account or payment destination identifier in a eryp o-eiirreney network or another piece of content that is accessible via the computer-based communications network,

9. ^'Hie computer-based method of claim L wherein the information relevant to the credibility of each respective one of the plurality of individuals includes, is at or is related to one or more of the following: a uniform resource locator; data relating to an online account, such as a social networking account, an e-commerce application account, a bank account, or a mobile application account; an electronic image, such as a picture from a passport or driver license; a video; file an audio file: a text or numeric data file, such as a passport or a license number, a bank account number, a social security number, a name, an address, an email address, a phone number, or a comment about content.

10. The computer-based method of claim 1 , further comprising:

accessing the information .from one or .more data sources: and

transforming the accessed information into one or more numerical measures, wherein the credibility of each individual is based, at least in part, on the numerical measures,

1 1. The computer-based method of claim 1. further comprising:

storing each credibility score in an electronic database.

12. The computer-based method of claim 1, wherein one or more of the endorsers are computers.

13. The computer-based method of claim I, wherein one or more of the endorsers are humans,

14. The computer-based method of claim 1, wherein the credibility score is represented by one or more score components.

.

15. The computer-based method of claim 1 , further comprising:

receiving scoring information from an. analysis engine; and

modifying one or more of the credibility scores based, at least in part, on the scoring information.

16. The computer-based method of claim 15. further comprising:

creating input variables,

wherein the analysis engine uses the input variables to create the scoring information.

17. The computer-based method of claim 15, further comprising:

creating training data,

wherein the analysis engine uses the training data to create the scoring information.

18. The computer-based method of claim 15, further comprising:

performing one or more quantitative analyses,

wherein the analysis engine uses the quantitative analyses to create the scoring information,

19. The computer-based method of claim 15, further comprising:

performing one or more quantitative analyses,

wherein the analysis engine dynamically chooses one of the quantitative analyses or a combination of the quantitative analyses to create the scoring information.

20. A computer system comprising:

a computer-based processing system; and

a plurality of computer-based user interface devices coupled to the computer- based processing system, via a computer-based communications network,

wherein the computer-based processing system comprises a processing device configured to:

enable each, respective one of a plurality of indi viduals to provide access to Information relevant to credibility of that individual;

assign a credibility score to one or more of the individuals based on the accessed information;

enable one or more of the individuals to associate with one or more network-based identifiers; and

receive, via the computer-based communications network, and based on a user's actions at one of the computer-based user interface devices, a message that identifies one of the network-based identifiers; and in response to receiving the message, returning via the computer-based communications network, the credibility score for one or more of the individuals associated with the network-based identifier in the message,

21 . The computer system of claim 20, wherein the computer-based processing system further compr ses memory storage configured to:

store each of the credibility scores in logical, association with corresponding ones of the plurality of individuals, and

store a unique identifier for each of the one or more imiquely-ideniifiahie network-based information in logical association with corresponding ones of the plurality of individuals,

22. A computer-based method comprising:

assigning a credibility score to each respective one of a plurality of individuals; receiving, at a computer-based server, an endorsement of a network-based identifier from one or more of the individuals via a computer-based communications network; and

assigning a credibility score to the neiwork-based identifier based, at least in part, on the credibility score of one or more of the individuals who endorsed the network- based identifier.

23. The computer-based method of claim 22. wherein assigning the credibility score to the network-based identifier is further based, at least in part, on other inibrmation about the network-based identifier itself

24. The computer-based method of claim 22, wherein the endorsement occurs in response to a user entering an endorsement at one of a plurality of computer-based user interface devices,

25. The computer-based method of claim 22, further comprising: receiving, at the computer-based server, a message that identifies one of the network-based identifiers; and

in response to receiving the message, returning via the computer-based communications network, the credibility score associated with the network-based identifier or a location identified in the message, and/or the credibility score for one or more of the individuals who endorsed the network-based identifier,

26. The computer-based method of claim 25, wherein the message occurs

automatically in response to a user query from a computer-based user interface.

27. The computer-based i.nethod of claim 25, wherein the credibility score returned in response to the received message is adapted to be rendered at a computer-based user- interface without revealing an identity of any of the individuals whose credibility score is being returned,

28. The computer-based method of claim 22, further comprising:

calculating the credibility score for each respective one of the individuals, wherein calculating the credibility score for each respective one of the individuals is based, at least in part, on online social media data associated with that individual and comes from one or more social media sources, or other information, via the computer- based communications network.

29. The computer-based method of claim 28, wherein calculating the credibility score for each respective one of the individuals further comprises:

enabling that individual to provide his or her open access authorization information for one or more of the social media sources on the computer-based communications network; and

utilizing the open access authorization information provided to access at least some of the online social media data associated with that individual.

30. The computer-based method of claim 29, wherein calculating the credibility score for each respective one of the individuals is based, at, least in part, on one or more endorsements of that individual from one or more other individuals,

31. The computer-based method of claim 30, wherein calculating the credibility score for each respective one of the individuals is based, at least in part, on one or more credibility scores associated with each respective one or more of the other individuals who have endorsed that individual.

32. The computer-based method of claim 22, wherein each one of the network-based identifiers is or is at:

a website URL, a webpage URL, an account ID or user name in a web

application, an account ID or user name in a mobile application, an account ID or user name in an SMS text application, an account ID or user name in a chat application, an email address, a public key in a public-key cryptographic network, an account ID or payment destination identifier in a crypio-currency network or another piece of content that Is accessible via the computer-based communications network.

33. A computer system comprising:

a computer-based processing system; and

a plurality of computer-based user interface devices coupled to the computer- based processing system, via a computer-based communications network.

assign a credibility score to each respective one of a plurality of individuals;

recei ve art. endorsement of a network-based identifier from one or more of the individuals via the computer-based communications network; and

assign a credibility score to the network-based identifier based, at least in part. on. the respective credibility scores of one or more of the individuals who endorsed the network-based identifier.

34. The computer system of claim 33, further comprising memory storage configured to store:

each credibility score of one of the individuals, and

each credibility score of one of the network-based identifiers.

35. The computer-implemented method of claim 22, wherein each credibility score Indicates a likelihood that a corresponding person or group who created or is associated with the network-based identifier is untrustworthy.

36. The computer-implemented method of claim 22, wherein each credibility score indicates a likelihood that the content available at or associated with the network-based identifier is untrustworthy.

37. The computer-implemented method of claim 22, wherein each credibility score indicates a likelihood that an individual associated with the network-based identifier satisfies criteria selected from the group consisting of: an identity-fraud risk criteria, a purchasing power criteria, a credit risk criteria, an insurance risk criteria, or a courtship criteria.

38. The computer-implemented method of claim 22, further comprising:

using an application programming interface to load one or more of the credibility scores to a first web-based information resource,

39. The computer-implemented method of claim 22, further comprising:

generating a limited-access URL, the selection of which by a selecting user, will; enable the selecting user to view the credibility score for the web-based

informati on resource only a limited number of times, and

wherein, after the selecting user selects the limited-access URL the limited number of times, the subsequent selection of the limited-access URL no longer enables any user to view the credibility score.

40. The computer-implemented method of claim 22, further comprising:

generating a limited- access URL, the selection of which by a selecting user, will: enable the selecting user to view the credibility score for the web-based information resource only a limited number of times, and

wherein, after the selecting user selects the limited-access URL the limited number of limes, the subsequent selection of the limited-access URL no longer enables the selecting user to view the credibility score.

41 . The computer-implemented method of claim 22, further comprising:

generating a limited-access URL, the selection of which by a selecting user, will: enable the selecting user to view the credibility score for the web-based information resource or the URL, and

wherein, after a limited number of users select the limited-access URL, the subsequent selection of the limited-access URL no longer enables any user to view the credibility score,

42. The computer-based method of claim 22, wherein the endorsement is a positive endorsement or a negative endorsement.

43. The computer-based method of claim 22, further comprising:

enabling each of the individuals to provide data relevant to credibility of each respective individual,

wherein the credibility of each respective individual is based, at least in part, on the provided data for that individual.

44. The computer-based method of claim 43, wherein the data relevant to the credibility of each respective endorser includes one or more of the following: a URL; an up-vote or down- vote of content; an evaluation of content or endorsers: data from an online account, such as a social networking account, an e-commerce application account, a mobile application account or a bank account; an electronic image, such as a picture from a passport or driver license; a video file; an audio file; a text or numeric data file, such as a passport or license number, a bank account number, a social security number, a name, an address, an email address, a phone number, or one or more comments about content.

45. The computer-based method of claim 22, further comprising:

accessing information from one or more data sources,

wherein the credibility of each respecti ve individual is based, at least in part, on the accessed information for that individual.

46. The computer-based method of claim 22, further comprising:

accessing information from one or more data sources associated with each respecti e indivi dual ,

wherein the credibility of each respective individual is based, at least in part, on the information accessed .for that, individual.

47. The computer-based .method of claim 46, wherein the one or more data sources include: a website, an online social network, an e-commerce application account, a mobile application account, an online bank account, a public record, endorser provided information, or an application programming interface.

48. The computer-based method of claim 22, further comprising:

accessing information from one or more data sources; and

transforming the accessed information into one or more numerical measures, wherein the credibility of each, respective individual is based, at least in part, on one or more of the numerical measures.

49. The computer-based method of claim 22, further comprising:

accessing information from one or more data sources associated with each respective individual; and transforming the accessed information into one or more numerical measures associated with each respective individual,

wherein the credibility of each respective individual is based, at least in part, on one or more of the numerical measures.

50. The computer-based method of claim 22, further comprising:

storing each credibility score in an electronic database,

51 . The computer-based method of claim 22, wherein one or more of the individuals are computers,

52. The computer-based method of claim 22, wherein one or more of the individuals are humans.

53. The computer-based method of claim 22, wherein each credibility score is represented by one or more score components.

54. The computer-based method of claim 22, further comprising:

receiving scoring information from an analysis engine, and

modifying the credibility score based, at least in part, on the scoring information.

55. The computer-based method of claim 54, further comprising:

creating input variables,

wherein the analysis engine uses the input variables to create the scoring information,

56. The computer-based method of claim 54, further comprising:

cre ing training data,

57. The computer-based method of claim 54, further comprising:

performing one or more quantitative analyses,

wherein the analysis engine uses the quantitative analyses to create the scoring information.

58. The computer-based method of claim 54, further comprising;

performing one or more quantitative analyses,

wherein the analysis engine dynamicalK' chooses one of the quantitative analyses or a combination of the quantitative analyses to create the scoring information,

59. A computer-implemented method comprising:

enabling a user to interact, via a first computer-based user interface device, with an element associated with a first web-based informaiion resource at a. URL or associated with the URL; and

in response the user interacting with the element, presenting to the user, via a computer-based communications network, specific information about the first web-based information resource,

wherein the specific informaiion includes credibility information about the first web-based information resource or the URL, and

wherein the credibility information is based, at least in part, on a credibility of one or more endorsers of the web-based informa tion resource or the URL,

60. The computer-implemented method of claim 59, wherein the element is selected from the group consisting of: a hyperlink and a form-suhmit-type button,

61 . The computer-based method of claim 59, wherein presenting to the user the specific information about the first web-based information resource or the URL comprises: presenting to the user a second web-based information resource that has the specific information about the first web-based information resouree or the URL-,

62. The computer-based method of claim 59, further comprising:

enabling a first web server to load the credibility score to the first web-based information resource,

wherein the first web server uses an application programming interface to load f credibility score to the first web-based information resource,

63. The computer-based method of claim 62, wherein the first web server

communicates information to a second web server, including a unique identifier of the first web-based information resource, a unique identifier for a user interacting with the first web-based information resource, and a URL.

64. A computer-implemented method comprising:

enabling a user to select a hyperlinked domain name at a first web-based information resource or URL via a first computer-based user interface device; and

in response to the user selecting said hyperlink, presenting to the user, via a computer-based communications network, a second web-based information resource that has specific information about the first web-based information resource or URL,

65. The computer-implemented method of claim 64,

wherein, in response to the user selecting the hyperlinked domain name, the second web-based informatio resource receives http-referer information identifying the first web-based information resource or URL; and

querying a database for information associated with the first web- based information resource or URL to present at a first or second web-based information resource.

A computer-implemented method comprising: enabling a user to interact with an element at a first web-based information resource via a first computer-based user interface device; and

in response the user interacting with the element, presenting to the user, a the first computer- baaed user interface device, specific information about the first web-based information resource,

wlierein the first web-based information resource is at an email application, chat, application, SMS text application, or mobile application,

wherein the specific information includes credibility information about the first web-based information resource, and

wherein the credibility information is based, at least in part, on credibility of one or more people who stave endorsed the first web-based information resource.

67. A computer-i mplemented system comprising:

a computer-based service server that includes a computer-based scoring engine, wherein the scoring engine is configured to calculate a credibility score for a web- based information resource, and

wherein the credibility score is based, at least in pari, on credibility of one or more endorsers of the web-based information resource or the URL.

68. The computer-implemented system of claim 67, wherein the credibility score indicates a likelihood that the individual who created or associated with the web-based information resource is untrustworthy.

69. The computer-implemented system of claim 67, wherein the credibility score indicates a likelihood that the web-based information resource is untrustworthy,

70. The computer-implemented system of claim 67, wherein the credibility score indicates a likelihood that the individual who created or associated with the web-based information resource satisfies certain criteria selected from the group consisting of: an identity- fraud risk criteria, a purchasing power criteria., a credit risk criteria, an insurance risk criteria, or a courtship criteria.

71.. The computer-implemented system of claim. 67, further comprising:

a first computer-based web server,

wherein the first web server uses an application programming interface to load the credibility score to the first web-based information resource.

72. The computer-implemented system of claim 67, wherei the service server is configured to:

generate a limited-access URL, the selection of which by a selecting user, will: enable the selecting user to view the credibility score for the web-based information resource or the URL only a limited number of times, and

wherein, after the selecting user selects the limited-access URL the limited number of times, the subsequent selection of the limited-access URL no longer enables the viewing of the credibility score.

73. The computer-implemented system, of claim 67, wherein the service server is configured to:

generate a limited-access URL, the selection of which by a selecting user, will; enable the selecting user to view the credibility score for the web-based information resource or the URL, and

wherein, after the selecting user selects the limited-access URL a limited number of times, the subsequent selection of the limited-access URL no longer enables the viewing of the credi bility score,

74. The computer-based system of claim 67, wherein the endorsement is a positive endorsement or a negative endorsement.

75. The computer-based system of claim 67, wherein the service server is configured to;

access information from one or more data sources, wherein the credibility of each respective endorser is based, at least in part, on the information.

76. The computer-based method of claim 75, wherein the one or more data sources include: a website, an online social network, an online bank account, a public record, endorser provided information, or an application programming interface.

77. The computer-based system of claim 75, further comprising:

a communication unit,

wherein the communication unit controls communication between the service server and the one or .more data sources.

78. A computer-implemented system comprising:

a computer-based service server; and

one or more computer-based user interface devices coupled to the service server, wherein the service server is configured to:

enable a user to interact from one of the user interface devices with an element associated with a first web-based information resource at a URL or associated with the URL; and

in response to the user interacting with the element, present to the user specific information about the first web-based information resource or the URL,

wherein the specific information includes credibility information about the first web-based information resource or the URL, and

wherein the credibility information is based, at least in part, on a credibility of one or more endorsers of the first web-based information resource or the URL.

79. The computer-implemented system of claim 78, wherein the element is selected from the group consisting of: a hyperlink and a form-submit-type button.

80. The computer-based system of claim 78, wherein presenting to the user specific information about the first web-based information resource comprises: presenting to the user a second web-based information resource that has the specific information about the first web-based information resource,

81. A computer-implemented system comprising:

a computer-based service server: and

enable a user to select a hyperlinked domain name at a first web-based

information resource; and

in. response the user selecting said hyperlink, present to the user a second web- based information resource that has specific information about die first web-based information resource.

82. The computer-implemented system of claim 81 ,

wherein, in response to the user selecting the hyperlinked domain name, the second web-based information resource receives http-referer information identifying the first web-based information resource; and

the service server queries a database for information associated with the first web- based information resource to present at the second web-based information resource.