Machine learning based webinterface production and deployment system

A computer-implemented deployment system for a customizable webinterface funnel, the funnel being customizable along a plurality of dimensions, comprising:

• a presentation database which identifies a frontend element value for each of at least one available non-graphical value of each of the dimensions of a plurality of elements of a webinterface including a logo, a main headline, a sub headline, and a call-to-action, wherein the presentation database is a rule repository with element rules, with each element rule including pointers identifying one-to-one mappings between N preset non-graphical dimension values and each of the dimensions of the plurality of elements including the logo, the main headline, the sub headline, and the call-to-action funnel, where N>1;
• a funnel customization specification memory storing, for a particular variation of the webinterface funnel, a non-graphical value for each of the dimensions of the plurality of elements including the logo, the main headline, the sub headline, and the call-to-action;
• and
• a presentation module that converts the dimensions of the plurality of elements including the logo, the main headline, the sub headline, and the call-to-action into the frontend element values based on the one-to-one mappings and presents toward a user a funnel having frontend element values indicated by the presentation database as corresponding to the non- graphical dimension values stored in the funnel customization specification memory.

A computer-implemented webinterface production and deployment system, comprising:

• a memory storing a database having a population of candidate individuals, each of the candidate individuals identifying a funnel of one or more webinterfaces, each of the webinterfaces having a plurality of elements including a logo, a main headline, a sub headline, and a call-to-action, each element in the plurality of elements having a plurality of dimensions along which a non-graphical value can be specified;
• and
• network server infrastructure which, in response to one or more first requests received over a network, returns toward a first client computer system:
• a presentation database which identifies a frontend element value for each of at least one available non-graphical value of each of the dimensions of the plurality of elements including the logo, the main headline, the sub headline, and the call-to-action, wherein the presentation database is a rule repository with element rules, with each element rule including pointers identifying one-to-one mappings between N preset non-graphical dimension values and each of the dimensions of the plurality of elements including the logo, the main headline, the sub headline, and the call-to-action, where N>1,
• a set of non-graphical dimension values identified by a first selected one of the candidate individuals in the population, and
• a presentation module which, in dependence upon the received set of non-graphical dimension values of the dimensions of the plurality of elements including the logo, the main headline, the sub headline, and the call-to-action, presents toward a user a funnel having frontend element values indicated by the received set of dimension values by converting the received set of non-graphical dimension values into the frontend element values based on the one-to-one mappings.

A computer-implemented webinterface production and deployment system, comprising:

• a memory storing a candidate database having a population of candidate individuals, each of the candidate individuals identifying a funnel of one or more webinterfaces, each of the webinterfaces having a plurality of elements including a logo, a main headline, a sub headline, and a call-to-action, each element in the plurality of elements having a plurality of dimensions along which a non-graphical value can be specified;
• and
• a machine learned content delivery network having accessibly thereto a presentation database which identifies a frontend element value for each of at least one available non-graphical value of each of the dimensions of the plurality of elements including the logo, the main headline, the sub headline, and the call-to-action funnel, the machine learned content delivery network being configured to, in response to a request from a client computer system, wherein the presentation database is a rule repository with element rules, with each element rule including pointers identifying one-to-one mappings between N preset non-graphical dimension values and each of the dimensions of the plurality of elements including the logo, the main headline, the sub headline, and the call-to-action, where N>1:
• retrieve from the database a selected one of the candidate individuals in the population, and
• delivers toward the client computer system a funnel having frontend element values indicated by the non-graphical dimension values of the selected candidate individual values by converting the non-graphical dimension values into the frontend element values based on the one-to-one mappings.

A computer-implemented webinterface production and deployment method, comprising:

• providing a memory storing a candidate database having a population of candidate individuals, each of the candidate individuals identifying a funnel of one or more webinterfaces, each of the webinterfaces having a plurality of elements including a logo, a main headline, a sub headline, and a call-to-action, each element in the plurality of elements having a plurality of dimensions along which a non-graphical value can be specified;
• providing a presentation database which identifies a frontend element value for each of at least one available non-graphical value of each of the dimensions of the plurality of elements including the logo, the main headline, the sub headline, and the call-to-action, wherein the presentation database is a rule repository with element rules, with each element rule including pointers identifying one-to-one mappings between N preset non-graphical dimension values and each of the dimensions of the plurality of elements including the logo, the main headline, the sub headline, and the call-to-action, where N>1;
• applying a selected one of the candidate individuals to the presentation database to determine frontend element values corresponding to non-graphical dimension values identified by the selected candidate individual by converting the non-graphical dimension values into the frontend element values based on the one-to-one mappings;
• and
• presenting toward a user a funnel having the determined frontend element values.