System, device, and method of protected electronic commerce and electronic financial transactions
System, device, and method of protected electronic commerce and electronic financial transactions. A method includes: analyzing (i) content of an online destination that sells an asset to an end-user, and (ii) data about ownership in the online destination, and (iii) meta-data about that content; and determining that an offering to sell that asset to the end-user is fraudulent. The system further provides digital tokens or crypto-currency, that end-users should pay in order to submit a user-report about a possible scam. The collected crypto-currency tokens are used by the system, to reward a user that submitted a scam report that turned out to be correct, to fund operations of a fraud-prevention entity, to fund guarantee payments to defrauded end-users, and for other purposes. The system generates authenticity stamps for online venues and for crypto-currency wallets that are determined to be legitimate.
1 . A method comprising:
(a) analyzing in combination at least
(i) content of an online destination, accessed by user electronic devices, that sells an asset or provides information to a user, and
(ii) data about ownership in said online destination, the analyzing being performed by one of the user electronic devices and/or by a remote server, and
collecting, at the remote server, from the user electronic devices, a plurality of genuine product votes or fraudulent product votes, that indicate that a particular product or service is or may be fraudulent;
aggregating, at the remote server, submissions or votes, by using a weighting mechanism,
based on the aggregated data, generating, at the one of the user electronic devices and/or the remote server, a possibly fraudulent risk score, which is used by a warning module to inform users about a possible risk of a fraudulent product or fraudulent service,
by performing, at the one of the user electronic devices and/or the remote server, an automatic risk analysis that is based on an online brand protection system that utilizes an artificial intelligence (AI) engine, that analyzes different web metrics of the online destination accessed by the user electronic devices to indicate a potential risk score for an online item being analyzed, and to determine if a product or service that is offered for sale is more-probably genuine or fraudulent,
wherein said web metrics that are analysed include at least some of:
content, keywords, web-page elements,
search engine optimization (SEO) data,
domain names in all top-level domains (TLDs),
a use of the brand name or a variation of the brand name or a typographical error of the brand name in a domain name),
hosting provider, domain name server (DNS)
data, registrar data,
traffic analytics, search engine results,
technologies and structures of the website or application,
meta-tags and coding utilized,
programming languages used,
use of logo or copyrighted pictures or trademarks,
inbound and outbound links,
engagement in social media,
linking from the website to social media or vice versa, and
amount and types of listings of a seller on a marketplace; and
(b) based on the analyzing of step (a), determining, at the one of the user electronic devices and/or the remote server, that a content on said online destination, which sells said asset or provides said information, is false or fraudulent.
2 . The method of claim 1 , further comprising:
providing to said user a notification that said user is about to purchase a counterfeit asset through said online destination.
3 . The method of claim 1 , further comprising:
providing to said user a notification that said user is about to purchase an authentic asset through said online destination; and providing to said user a money-back guarantee that said asset is authentic.
4 . The method of claim 1 , further comprising:
publishing into a block-chain data-set at least one of: (i) said user-submitted reports, (ii) an indication of said consensus;
providing a reward to at least one user that submitted a report that matches the consensus.
5 . The method of claim 1 , wherein the analyzing of step (a) analyzes content of an online destination that sells an asset, and comprises:
(i) determining that a first entity which is the registered owner of said online destination;
(ii) determining that a second, different, entity is the registered owner of a trademark in said asset that is offered for sale through said online destination;
(iii) based on the first entity being different from the second entity, determining that an offer for sale of said asset on said online destination is fraudulent.
6 . The method of claim 1 ,
wherein the analyzing of step (a) analyzes content of an online destination that sells an asset, and comprises at least:
(i) determining a first price at which said online destination offers said asset for sale to said end-user;
(ii) determining a second price which is a manufacturer suggested retail price (MSRP) of said asset;
(iii) determining that the first price is smaller than the second price by at least N percent of the second price, wherein N is a pre-defined positive number;
(iv) based on step (iii), determining that an offer for sale of said asset on said online destination is fraudulent.
7 . The method of claim 6 ,
wherein the analyzing of step (a) analyzes content of an online destination that sells an asset, and comprises at least:
(i) determining that a textual description of said asset on said online destination, has one or more discrepancies relative to a formal description of said asset by a manufacturer of said asset;
(ii) based on step (i), determining that an offer for sale of said asset on said online destination is fraudulent.
8 . The method of claim 1 ,
wherein the analyzing of step (a) analyzes content of an online destination that sells an asset, and comprises at least:
(i) determining that textual description of said asset on said online destination, has one or more different characteristics relative to a formal description of said asset by a manufacturer of said asset; wherein said characteristics comprise one or more of: size, dimensions, weight, color, materials used, quantity;
(ii) based on step (i), determining that an offer for sale of said asset on said online destination is fraudulent.
9 . The method of claim 1 ,
wherein the determining of step (b) is also based on a determination that said online destination is owned by a particular entity that was already identified as an owner of another online destination that was determined to be fraudulent.
10 . The method of claim 9 ,
wherein the analyzing of step (a) comprises at least:
(i) receiving a plurality of user-submitted reports that indicate that said online destination offers a fraudulent sale of one or more assets or contains false information;
wherein each user-submitted report is received, exclusively, via a submission mechanism that necessarily requires a cryptocurrency payment by a submitting user together with submitting his report;
(ii) performing the determining of step (b) by taking into account at least a consensus that is detected among a crowd of users with regard to correctness of one or more of said user-submitted reports, based on a pre-defined rule that indicates one or more conditions for reaching consensus.
11 . The method of claim 1 , comprising:
creating a bounty source from multiple payments received together with multiple user-submitted reports;
if a particular user-submitted report of a particular submitting-user is determined to be correct, then: distributing to said particular submitting-user a payment that is a portion of said bounty source, in an amount that is equal to or greater than the payment that said particular submitting-user had paid together with his user-report submission.
12 . The method of claim 1 , comprising:
(i) analyzing content and meta-data of an online venue that receives cryptocurrency payments into a cryptocurrency wallet; and detecting a mismatch between (I) the actual identity of the real-life holder of said cryptocurrency wallet, and (II) the identity of the real-life owner of said online venue;
(ii) based on said mismatch, notifying an end-user that said online venue, which receives cryptocurrency payments into said cryptocurrency wallet, is fraudulent.
13 . The method of claim 1 , comprising:
publishing into a block-chain data-set at least one of: (i) said N user-submitted reports, (ii) said M user-submitted reports, (iii) an indication of said consensus;
providing a reward to: at least one user that submitted at least one of said M user-submitted reports that said particular product is authentic, or,
to at least one user that submitted at least one of said N user-submitted reports that said particular product is counterfeit.
14 . A system, comprising a remote server, configured to:
(a) analyze in combination at least
i) content of an online destination, accessed by user electronic devices, that sells an asset or provides information to a user, and
(ii) data about ownership in said online destination,
the analyzing being performed by the remote server, and
collect, at the remote server, from the user electronic devices, a plurality of genuine product votes or fraudulent product votes, that indicate that a particular product or service is or may be fraudulent;
aggregate, at the remote server, submissions or votes, by using a weighting mechanism,
based on the aggregated data, generate, at the remote server, a possibly fraudulent risk score, which is used by a warning module to inform users about a possible risk of a fraudulent product or fraudulent service,
by performing, at the remote server, an automatic risk analysis that is based on an online brand protection system that utilizes an artificial intelligence (AI) engine, that analyzes different web metrics of the online destination accessed by the user electronic devices to indicate a potential risk score for an online item being analyzed, and to determine if a product or service that is offered for sale is more-probably genuine or fraudulent,
wherein said web metrics that are analysed include at least some of:
content, keywords, web-page elements,
search engine optimization (SEO) data,
domain names in all top-level domains (TLDs),
a use of the brand name or a variation of the brand name or a typographical error of the brand name in a domain name),
hosting provider, domain name server (DNS) data, registrar data,
traffic analytics, search engine results,
technologies and structures of the website or application,
meta-tags and coding utilized,
programming languages used,
use of logo or copyrighted pictures or trademarks,
inbound and outbound links,
engagement in social media,
linking from the website to social media or vice versa, and
amount and types of listings of a seller on a marketplace; and
(b) based on the analyzing of step (a), determine, at the remote server, that a content on said online destination, which sells said asset or provides said information, is false or fraudulent.
15 . A computer program product comprising at least one computer readable non-transitory storage medium containing program instructions, which program instructions when read by processing circuitry of a remote server, cause the processing circuitry to perform a method comprising:
(a) analyzing in combination at least
(i) content of an online destination, accessed by user electronic devices, that sells an asset or provides information to a user, and
(ii) data about ownership in said online destination,
the analyzing being performed by the remote server, and
collecting, at the remote server, from the user electronic devices, a plurality of genuine product votes or fraudulent product votes, that indicate that a particular product or service is or may be fraudulent;
aggregating, at the remote server, submissions or votes, by using a weighting mechanism,
based on the aggregated data, generating, at the remote server, a possibly fraudulent risk score, which is used by a warning module to inform users about a possible risk of a fraudulent product or fraudulent service,
by performing, at the remote server, an automatic risk analysis that is based on an online brand protection system that utilizes an artificial intelligence (AI) engine, that analyzes different web metrics of the online destination accessed by the user electronic devices to indicate a potential risk score for an online item being analyzed, and to determine if a product or service that is offered for sale is more-probably genuine or fraudulent,
wherein said web metrics that are analysed include at least some of:
content, keywords, web-page elements,
search engine optimization (SEO) data,
domain names in all top-level domains (TLDs),
a use of the brand name or a variation of the brand name or a typographical error of the brand name in a domain name),
hosting provider, domain name server (DNS) data, registrar data,
traffic analytics, search engine results,
technologies and structures of the website or application,
meta-tags and coding utilized,
programming languages used,
use of logo or copyrighted pictures or trademarks,
inbound and outbound links,
engagement in social media,
linking from the website to social media or vice versa, and
amount and types of listings of a seller on a marketplace; and
(b) based on the analyzing of step (a), determining, at the remote server, that a content on said online destination, which sells said asset or provides said information, is false or fraudulent.