# US 2019/0244131 A1 · Method and system for applying machine learning approach to routing webpage traffic based on visitor attributes > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0244131 A1 - Document ID: US20190244131A1 - Status: Published application - Kind: A1 - Application number: 16/265,142 - Filed: 2019-02-01 - Published: 2019-08-08 - Earliest claimed: 2018-02-02 - Assignees: Unbounce Marketing Solutions Incorporated - Applicants: Unbounce Marketing Solutions Incorporated - Inventors: Thomas Scott LEVI; Jordan Tyler DAWE; Yosem Simon REICHERT-SWEET; Michael Brendan McDERMOTT - Web page: https://patentgrep.com/patent/US20190244131A1 ## CPC - G06F 16/958: Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking - G06N 20/00: Machine learning - G06N 3/09: Supervised learning - G06N 5/04: Inference or reasoning models - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N 3/08: Learning methods - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract The present invention is a cloud-based machine learning method and system that utilizes the attributes and past performance statistics of visitors to a set of webpage variants to predict performance statistics for incoming website visitors with respect to the webpage variants, and uses such predicted performance statistics to direct such incoming website visitors; and which learns from the performance of each directed website visitor by refining the past performance statistics to take into account such performance and the attributes of each directed website visitor, all in order to optimize future performance statistics for the set of webpage variants. ## Claims ### Claim 1 A computer-implemented method for routing Internet traffic for a target webpage, wherein the target webpage has a plurality of webpage variants, and wherein the Internet traffic comprises a plurality of new visitors, comprising: at a server: (i) receiving a request for the target webpage from a new visitor; (ii) receiving at least one attribute for said new visitor; (iii) calculating for each webpage variant, a predicted performance statistic and optionally an uncertainty in relation to the new visitor, based upon the at least one attribute of the new visitor and based upon a performance history for each webpage variant, the performance history including a performance statistic in relation to visitor attributes; (iv) routing the new visitor to a routed webpage variant, based upon an exploit/explore strategy, wherein the routed webpage variant is one of the webpage variants; (v) determining an actual performance outcome for the routed webpage variant in respect of the new visitor; (vi) updating the performance history by incorporating the actual performance outcome for the routed webpage variant in respect of the new visitor and by incorporating the at least one attribute of the new visitor; and (vii) repeating steps (i) to (vi) for each subsequent new visitor. ### Claim 2 A computer-implemented method for routing Internet traffic for a target webpage, wherein the target webpage has a plurality of webpage variants, and wherein the Internet traffic comprises a plurality of new visitors, comprising: at a server: (i) receiving a request for the target webpage from a new visitor; (ii) receiving at least one attribute for said new visitor; (iii) determining for each webpage variant a performance history, the performance history including a performance statistic in relation to visitor attributes; (iv) calculating for each webpage variant, a predicted performance statistic and optionally an uncertainty in relation to the new visitor, based upon the at least one attribute of the new visitor and based upon the performance history; (v) routing the new visitor to a routed webpage variant, based upon an exploit/explore strategy, wherein the routed webpage variant is one of the webpage variants; (vi) determining an actual performance outcome for the routed webpage variant in respect of the new visitor; (vii) updating the performance history by incorporating the actual performance outcome for the routed webpage variant in respect of the new visitor and by incorporating the at least one attribute of the new visitor; and (viii) repeating steps (i) to (vii) for each subsequent new visitor. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the at least one attribute is selected from the group consisting of: visitor device operating system type; desktop or mobile user; visitor browser type; IP address; internet service provider; visitor geographic location; server's geographic location; visitor age demographic; visitor firmographic attribute; visitor browser language; referrer channel; and Urchin Tracking Module parameters. ### Claim 4 (depends on 3) The computer-implemented method of claim 3, wherein the at least one attribute for the new visitor is automatically detected by the server. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the performance statistic reflects webpage conversion rate. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein the performance statistic reflects webpage click-through rates or user lead generations. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the predicted performance statistic is calculated using one or more modeling techniques selected from of the group consisting of: Naïve Bayes; Hierarchical Bayes; Neural Networks; Linear Regression; and Regression Trees. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, wherein the uncertainty is calculated using one or more modeling techniques selected from of the group consisting of: Monte Carlo sampling; bootstrapping; and propagation of uncertainty. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein the exploit/explore strategy is determined from using one or more of strategies selected from the group consisting of: Thompson sampling; epsilon-greedy strategy; epsilon-decreasing strategy; Monte Carlo simulation and Upper Confidence Bound. ### Claim 10 (depends on 1) The computer-implemented method of claim 1, wherein the exploit/explore strategy involves maximising the predicted performance statistic. ### Claim 11 (depends on 1) The computer-implemented method of claim 1, wherein the step of calculating for each webpage variant a predicted performance statistic and an optionally uncertainty in relation to the new visitor, is additionally calculated based on a plurality of learned model parameters; and wherein the step of updating the performance history by incorporating the actual performance outcome for the routed webpage variant in respect of the new visitor and by incorporating the at least one attribute of the new visitor additionally comprises: determining the learned model parameters for each webpage variant. ### Claim 12 (depends on 11) The computer-implemented method of claim 11, wherein the determining the learned model parameters is performed by a modelling method selected from the group consisting of Bayesian Inference, Maximum Likelihood Estimation and Stochastic Gradient Descent. ### Claim 13 A communication system for routing Internet traffic for a target webpage, wherein the target webpage has a plurality of webpage variants, and wherein the Internet traffic comprises a plurality of new visitors, the system comprising: a web-based communication network; a plurality of communication devices coupled to the communication network; and a server coupled to the communication network, wherein the server comprises a processor configured with executable instructions to perform operations comprising: at the server: (i) receiving a request for the target webpage from a new visitor; (ii) receiving at least one attribute for said new visitor; (iii) calculating for each webpage variant, a predicted performance statistic and optionally an uncertainty in relation to the new visitor, based upon the at least one attribute of the new visitor and based upon a performance history for each webpage variant, the performance history including a performance statistic in relation to visitor attributes; (iv) routing the new visitor to a routed webpage variant, based upon an exploit/explore strategy, wherein the routed webpage variant is one of the webpage variants; (v) determining an actual performance outcome for the routed webpage variant in respect of the new visitor; (vi) updating the performance history by incorporating the actual performance outcome for the routed webpage variant in respect of the new visitor and by incorporating the at least one attribute of the new visitor; and (vii) repeating steps (i) to (vi) for each subsequent new visitor. ### Claim 14 A non-transitory computer readable storage medium having stored thereon processor-executable instructions configured to cause a processor to perform operations for routing Internet traffic for a target webpage, wherein the target webpage has a plurality of webpage variants, and wherein the Internet traffic comprises a plurality of new visitors, the operations comprising: at the server: (i) receiving a request for the target webpage from a new visitor; (ii) receiving at least one attribute for said new visitor; (iii) calculating for each webpage variant a predicted performance statistic and an uncertainty in relation to the new visitor, based upon the at least one attribute of the new visitor and based upon a performance history for each webpage variant, the performance history including a performance statistic in relation to visitor attributes; (iv) routing the new visitor to a routed webpage variant, based upon an exploit/explore strategy, wherein the routed webpage variant is one of the webpage variants; (v) determining an actual performance outcome for the routed webpage variant in respect of the new visitor; (vi) updating the performance history by incorporating the actual performance outcome for the routed webpage variant in respect of the new visitor and by incorporating the at least one attribute of the new visitor; and (vii) repeating steps (i) to (vi) for each subsequent new visitor.