US 11,900,365 B1
Predicting attributes for recipients
Yaakov Tayeb, Tzur Hadassa (IL); and Hadar Lackritz, Tel-Aviv (IL)
Assigned to Intuit, Inc., Mountain View, CA (US)
Filed by INTUIT INC., Mountain View, CA (US)
Filed on Oct. 26, 2022, as Appl. No. 18/049,846.
Int. Cl. G06Q 20/38 (2012.01)
CPC G06Q 20/38 (2013.01) 18 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving, by one or more processors, electronic transaction data indicative of one or more transactions, wherein the one or more transactions are associated with one or more unique providers, and wherein each transaction of the one or more transactions indicates a recipient and one unique provider;
identifying, by the one or more processors and from the one or more transactions, a subset of transactions that are associated with known values for an attribute with respect to one or more unique recipients, and wherein each transaction of the subset of transactions indicates one unique provider and one unique recipient;
computing, by the one or more processors and for each unique provider of the one or more unique providers, a provider feature based on the known values for the attribute with respect to a subset of the one or more unique recipients associated with the unique provider in the subset of transactions;
computing, by the one or more processors and for a given recipient indicated in one or more given transactions of the one or more transactions that are not included in the subset of transactions, a recipient feature based on the provider feature of each unique provider of the one or more unique providers that is indicated in the one or more given transactions, the computing the recipient feature comprising:
combining the provider features of each unique provider of the one or more unique providers that is indicated in the one or more given transactions to generate a combined provider feature, and
normalizing the combined provider feature based on a sum of components of the combined provider feature; and
predicting, by the one or more processors, based on the recipient feature and via a machine learning model, a value for the attribute with respect to the given recipient.