Granted Patent B2

US 12,476,981 · App. 18/257,463 · Granted Nov 18, 2025

Inventors: Stewart Lawrence Boutcher (London, GB); Michael Anthony Townend (London, GB); Nigel Paul Bridges (London, GB)

Assignee: BEACONSOFT LIMITED

H04L63/1408H04L2463/144

Abstract

Claims (63)

1 . A user digital journey validation method comprising the steps of:

a) connecting, by a fully distributed blockchain computer system, a trust network comprising: a plurality of user nodes; a plurality of trusted party nodes; a visitor node corresponding to a visitor to a digital service; and a plurality of links, the user nodes corresponding to users, the trusted party nodes corresponding to trusted parties, the visitor node being the most recent node in the trust network, the links being the connections between nodes;

b) rating, by the trust network, the visitor node, the rating being a visitor trust score;

c) recording, via a digital journey mapping system, a digital journey of the visitor;

d) analysing, by the AI system, the digital journey of the visitor;

e) detecting, by the AI system, bot-like behaviour associated with the visitor node or the user nodes;

f) assigning, by the AI system, a warning flag to the visitor node or the user if the visitor node or user node has associated bot-like behaviour;

g) removing, by the AI system, any fraudulent nodes in the trust network, the fraudulent nodes being user nodes or visitor nodes having associated warning flags;

h) updating, by the trust network, the visitor trust score based on the analysis results of the digital journey and the removal of any links connected to or from fraudulent nodes; and

i) providing, by the AI system, a signal or value indicative of a degree of trustworthiness, to the digital service.

2 . The method according to claim 1 , wherein the visitor trust score is based on: the number of user nodes;

user trust weightings each corresponding to a respective user node;

the number of trusted party nodes; and

trusted party trust weightings each corresponding to a respective trusted party node.

3 . The method according to claim 1 , wherein the digital journey of the visitor comprising a series of visitor interactions between the visitor and the said digital service, the said digital service being digital content.

4 . A computer program product including a program for a processing device, the computer program product comprising a non-transitory computer-readable medium on which software code portions are stored, the software code portions configured for performing the steps comprising:

a) connecting, by a fully distributed blockchain computer system, a trust network comprising: a plurality of user nodes; a plurality of trusted party nodes; a visitor node corresponding to a visitor to a digital service; and a plurality of links, the user nodes corresponding to users, the trusted party nodes corresponding to trusted parties, the visitor node being the most recent node in the trust network, the links being the connections between nodes;

b) rating, by the trust network, the visitor node, the rating being a visitor trust score;

c) recording, via a digital journey mapping system, a digital journey of the visitor;

d) analysing, by the AI system, the digital journey of the visitor;

e) detecting, by the AI system, bot-like behaviour associated with the visitor node or the user nodes;

f) assigning, by the AI system, a warning flag to the visitor node or the user if the visitor node or user node has associated bot-like behaviour;

g) removing, by the AI system, any fraudulent nodes in the trust network, the fraudulent nodes being user nodes or visitor nodes having associated warning flags;

h) updating, by the trust network, the visitor trust score based on the analysis results of the digital journey and the removal of any links connected to or from fraudulent nodes; and

i) providing, by the AI system, a signal or value indicative of a degree of trustworthiness, to the digital service, when the program is run on the processing device.

5 . A computer program product according to claim 4 , wherein the program is directly loadable into an internal memory of the processing device.

6 . A user journey validation system for detecting and eliminating fraudulent activity, the user journey validation system comprising:

a trust network;

a fully distributed blockchain computer system;

a digital journey mapping system; and

an AI system;

wherein the trust network comprises:

a trust network comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between the nodes representing a single degree of separation between the nodes, the nodes comprising: a visitor node corresponding to a visitor; one or more user nodes, each corresponding to a user; and one or more trusted party nodes, each corresponding to a trusted party; and

a trust network score;

wherein the fully distributed blockchain computer system is configured to:

continuously update the trust network in response to an interaction between a digital service and a visitor; implement the trust network using a blockchain ledger; and

store the trust network in the blockchain ledger;

wherein the digital journey mapping system is configured to anonymously record a digital journey of the visitor, the digital journey comprising a series of visitor interactions between the visitor and the digital service, the digital service being digital content; and

wherein the AI system is configured to:

analyse the digital journey and provide analysis results to the trust network such that the trust network is arranged to change the trust network score according to the analysis results.

7 . The system of claim 6 , wherein the visitor node is arranged to be the most recent node on the trust network.

8 . The system of claim 6 , wherein the user nodes each comprise: an associated user trust weighting and an associated user trust score and wherein the trusted party nodes each comprise: an associated trusted party trust weighting and an associated trusted party trust score.

9 . The system of claim 8 , wherein the trust network score is calculated based on:

the number of user nodes in the trust network;

the number of trusted party nodes in the trust network;

the user trust weighting associated with each of the user nodes in the trust network;

the trusted party trust weighting associated with each of the trusted party nodes in the trust network; and

the analysis results.

10 . The system of claim 6 , wherein the visitor node comprises an associated visitor trust score, the visitor trust score corresponding to the trust network score.

11 . The system of claim 6 , wherein the digital service comprises a trust threshold.

12 . The system of claim 11 , wherein the visitor can gain access to the digital service if the corresponding visitor trust score satisfies the trust threshold.

13 . The system of claim 11 wherein the visitor is presented with a verification step if the visitor trust score does not satisfy the trust threshold as defined solely by the digital service.

14 . The system of claim 13 , wherein either one of: the user does not gain access to the digital service or a warning flag is associated with the user if the visitor fails the verification step.

15 . The system of claim 6 , wherein the digital journey mapping system is further configured to store a visitor engagement indicator, the visitor engagement indicator indicating visitor engagement with content items.

16 . The system of claim 6 , wherein the AI system is arranged to scan multiple digital services and multiple digital journeys simultaneously.

17 . The system of claim 6 , wherein the AI system is further configured to detect suspicious behaviour of the users associated with the user nodes and create a warning flag in response to the suspicious behaviour.

18 . The system of claim 17 , wherein the suspicious behaviour comprises:

bot-like behaviour; or

identity fraud.

19 . The system claim 18 , wherein the AI system is configured to remove any user nodes or visitor nodes each associated with one or more warning flags in the trust network and update the network trust score and the user trust scores of the trust network.

20 . The system of claim 18 , wherein the AI system removes any user nodes or visitor nodes each associated with one or more warning flags in real time.

21 . The system of claim 17 , wherein the warning flag is associated with the visitor node or the one or more user nodes that exhibited the suspicious behaviour.

22 . The system of claim 6 , wherein the AI system comprises a machine learning AI-engine.

Assignments (2)

Continuity (1)

References Cited (25)