Efficient scaling of condition existence evaluation through human and/or artificial neural network allocation

A method for efficiently allocating requests for electronic determination of existence of conditions, the method comprising:

• receiving a first evaluation request to determine existence of a first condition comprising a first condition data indicating the existence the first condition,
• wherein the first condition data received from a device of a first user alleging existence of the first condition;
• extracting a first evaluation criteria data from a first condition profile;
• selecting a first evaluation tier to evaluate the first condition data comprising any one of (i) one or more human users, and (ii) an artificial neural network;
• receiving a second evaluation request to determine existence of a second condition comprising a second condition data indicating the existence the second condition,
• wherein the second condition data received from a device of a second user alleging existence of the second condition;
• referencing a parameter defining a capacity of an evaluation load based on resource constraints comprising at least one of:
• (i) a number of available human users acting as evaluators;
• and (ii) available computing resources powering the artificial neural network;
• determining an evaluation load of the first evaluation tier exceeds evaluation capacity of the first evaluation tier;
• and
• selecting a second evaluation tier for evaluation of the second condition data to load balance incoming evaluation requests, the second evaluation tier comprising (i) the artificial neural network if the first evaluation tier comprises the one or more human users to reduce use of human evaluation, and (ii) the one or more human users if the first evaluation tier comprises the artificial neural network to conserve computing resources.

A computer readable media that is non-transitory usable for efficiently allocating requests for electronic evaluation of existence conditions, the computer readable media comprising computer readable instructions that when executed:

• receive a first evaluation request to determine existence of a first condition comprising a first condition data indicating the existence the first condition,
• wherein the first condition data received from a device of a first user alleging existence of the first condition;
• extract a first evaluation criteria data from a first condition profile;
• select a first evaluation tier to evaluate the first condition data comprising any one of (i) one or more human users, and (ii) an artificial neural network;
• receive a second evaluation request to determine existence of a second condition comprising a second condition data indicating the existence the second condition,
• wherein the second condition data received from a device of a second user alleging existence of the second condition;
• reference a parameter defining a capacity of an evaluation load based on resource constraints comprising at least one of:
• (i) an number of available human users acting as evaluators;
• and (ii) available computing resources powering the artificial neural network;
• determine an evaluation load of the first evaluation tier exceeds evaluation capacity of the first evaluation tier;
• and
• select a second evaluation tier for evaluation of the second condition data to load balance incoming evaluation requests, the second evaluation tier comprising (i) the artificial neural network if the first evaluation tier comprises the one or more human users to reduce use of human evaluation, and (ii) the one or more human users if the first evaluation tier comprises the artificial neural network to conserve computing resources.