Patent ID: 11860880
Assignee: WALMART APOLLO, LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 14:
15. The method of claim 12, further comprising:
identifying, using the optimization algorithm of the feature learning system, one or more sub-populations of the case individuals from the gross population of the case individuals, wherein the one or more sub-populations of the case individuals comprise the first sub-population of the case individuals, and wherein identifying the one or more sub-populations of the case individuals comprises:
identifying a second sub-population of the case individuals from the gross population of the case individuals, wherein the one or more sub-populations of the case individuals comprise the second sub-population of the case individuals, wherein the second sub-population of the case individuals are associated with the at least one first sub-population feature and at least one second sub-population feature, wherein the at least one second sub-population feature differs from the at least one first sub-population feature, and wherein identifying the second sub-population of the case individuals from the gross population of the case individuals comprises:
identifying a second control sub-population of the case individuals from the gross population of the case individuals, wherein identifying the second control sub-population of the case individuals from the gross population of the case individuals comprises grouping together third case individuals randomly selected from the gross population of the case individuals to form the second control sub-population of the case individuals, and wherein the third case individuals are associated with the at least one first sub-population feature and the at least one second sub-population feature;
identifying a second test sub-population of the case individuals from the gross population of the case individuals, wherein identifying the second test sub-population of the case individuals from the gross population of the case individuals comprises grouping together fourth case individuals randomly selected from the gross population of the case individuals to form the second test sub-population of the case individuals, wherein the third case individuals are exclusive from the fourth case individuals, wherein the fourth case individuals are associated with the at least one first sub-population feature and the at least one second sub-population feature, and wherein the third case individuals and the fourth case individuals together comprise the second sub-population of the case individuals;
presenting second control content to the second control sub-population of the case individuals, wherein the second control content is selected according to the first statistical model;
measuring a second control sub-population average feedback metric based on a second control content feedback provided from the second control sub-population of the case individuals in response to being presented the second control content;
presenting a second test content to the second test sub-population of the case individuals, wherein the second test content is selected according to a third statistical model different than the first statistical model and the second statistical model;
measuring a second test sub-population average feedback metric based on a second test content feedback from the second test sub-population of the case individuals in response to being presented the second test content; and
determining that the second test sub-population average feedback metric exceeds the second control sub-population average feedback metric of the second control sub-population of the case individuals; and
determining that a probability value for a difference between the second test sub-population average feedback metric and the second control sub-population average feedback metric is less than the predetermined significance level value.