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

Claim 13:
14. The method 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 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 a second control population 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 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 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 the first control content to the second control sub-population of the case individuals;
measuring a second control sub-population average feedback metric based on the first control content feedback provided from the second control sub-population of the case individuals in response to being presented the first control content;
presenting the first test content to the second test sub-population of the case individuals;
measuring a second test sub-population average feedback metric based on of the first test content feedback provided from the second test sub-population of the case individuals in response to being presented the first test content; and
determining that the second test sub-population average feedback metric exceeds a second control population average feedback metric of the second control sub-population of the case individuals, wherein the second control sub-population of the case individuals are distinct from the second test 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.