Walmart Apollo, LLC

Paridhi Kabra

Jingying Zhang

Prateek Mishra

Shilka Roy

Ravi Shankar Karedla

Nitin Kapoor

Systems, methods, and computer-readable media are configured to auto-populate pharmacy SIG codes based on pattern matching model in a pharmacy system. The prescriptions including one or more fields of prescription information in natural language are received in the system. The prescription information can be parsed according to rule-based pattern matching model. The parsed prescription information can be mapped to a token SIG using rule-based pattern matching model dictionaries. A prescription SIG code based on each of the token SIG can be generated. A confidence value of the prescription SIG code based on character coverage of the prescription can be calculated.

Pharmacy SIG codes auto-populating system

Systems and methods for determining whether a particular feature or change implemented in at least one test store causes a significant change as compared to one or more control stores are discussed. More particularly, techniques for using a time-series clustering algorithm to identify comparable sister stores to a store in which a feature change is being considered are described. Once the sister stores are identified a testing module can perform an A/B testing so as to validate whether a particular feature change being implemented in the test store causes a significant change as compared to the control stores.

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Indore, India

Paridhi Kabra