System and method for group payments

Abstract

Based on detecting a selection, by a first user, of a group payment option on a merchant interface, the computer system launches a user selection user interface that includes one or more user selection interface elements that correspond to one or more users associated with the first user. Based on detecting a selection of one or users, the computer system launches a group payment user interface that includes one or more payment allocation user interface elements that corresponds to a payment allocation for the selected users. Based on detecting a confirmation of a first payment allocation plan, the computer system processes a payment for the purchase and further transmits invoices to the selected users which include an allocated payment amount.

1 . (canceled) 
     
     
         2 . A method, comprising:
 receiving, at a trained second classifier module of a computer system, an indication of a pending transaction initiated by a user computing device,   wherein a first classifier module was trained using first data for a set of completed transactions as training data input, wherein the first data includes, for respective transactions in the set of completed transactions both pre-transaction information and post-transaction information, wherein labeled classifications for transactions in the set of completed transactions are known, and wherein the post-transaction information for a first transaction in the set of completed transactions includes information for one or more transactions performed subsequent to a completion of the first transaction and non-transaction activity occurring subsequent to the completion of the first transaction for a user associated with the first transaction,   wherein the trained second classifier module was trained using operations comprising:
 generating respective weights for multiple transactions in the set of completed transactions based on classification outputs of the trained first classifier for the multiple transactions; and 
 training, based on the generated weights, a second classifier module using second data for the set of completed transactions as training data input, wherein the second data for the set of completed transactions includes pre-transaction information for transactions in the set of completed transactions; 
   classifying, using the trained second classifier module of the computer system, the pending transaction based on pre-transaction information for the pending transaction; and   the computer system generating, based on a classification output by the trained second classifier for the pending transaction, an authorization decision for the pending transaction, wherein the authorization decision specifies an indication of approval or non-approval for the pending transaction.   
     
     
         3 . The method of  claim 2 , wherein the post-transaction information includes location data for a plurality of user devices. 
     
     
         4 . The method of  claim 2 , wherein the post-transaction information includes Internet browsing history data associated with a plurality of user devices. 
     
     
         5 . The method of  claim 2 , wherein the post-transaction information includes textual data input by a plurality of users associated with the post-transaction information. 
     
     
         6 . The method of  claim 2 , wherein the pending transaction is an electronic purchase transaction initiated by the user computing device via a software program executing on the user computer device. 
     
     
         7 . The method of  claim 2 , wherein the training based on the generated weights includes one or more of:
 performing a greater number of training iterations for a first transaction than for a second transaction based on the first transaction having a greater weight than the second transaction; or   performing a greater training adjustment for a first transaction than for a second transaction based on the first transaction having a greater weight than the second transaction.   
     
     
         8 . The method of  claim 2 , wherein the generating the respective weights is based on relationships between labeled classifications and output predictions generated by the trained first classifier module for the transactions. 
     
     
         9 . The method of  claim 2 , wherein the generating the respective weights provides greater weights for transactions whose output predictions are further from a labeled classification. 
     
     
         10 . A non-transitory computer-readable medium having stored thereon instructions that are executable by a computer system having a processor and a memory to cause the computer system to perform operations comprising:
 receiving, at a trained second classifier module of the computer system, an indication of a pending transaction initiated by a user computing device,   wherein a first classifier module was trained using first data for a set of completed transactions as training data input, wherein the first data includes, for respective transactions in the set of completed transactions both pre-transaction information and post-transaction information, wherein labeled classifications for transactions in the set of completed transactions are known, and wherein the post-transaction information for a first transaction in the set of completed transactions includes information for one or more transactions performed subsequent to a completion of the first transaction and non-transaction activity occurring subsequent to the completion of the first transaction for a user associated with the first transaction,   wherein the trained second classifier module was trained using operations comprising:
 generating respective weights for multiple transactions in the set of completed transactions based on classification outputs of the trained first classifier for the multiple transactions; and 
 training, based on the generated weights, a second classifier module using second data for the set of completed transactions as training data input, wherein the second data for the set of completed transactions includes pre-transaction information for transactions in the set of completed transactions; 
   classifying, using the trained second classifier module of the computer system, the pending transaction based on pre-transaction information for the pending transaction; and   the computer system generating, based on a classification output by the trained second classifier for the pending transaction, an authorization decision for the pending transaction, wherein the authorization decision specifies an indication of approval or non-approval for the pending transaction.   
     
     
         11 . The non-transitory computer-readable medium of  claim 10 , wherein the post-transaction information includes textual data input by a plurality of users associated with the post-transaction information. 
     
     
         12 . The non-transitory computer-readable medium of  claim 10 , wherein the pending transaction is an electronic purchase transaction initiated by the user computing device via a software program executing on the user computer device. 
     
     
         13 . The non-transitory computer-readable medium of  claim 10 , wherein the training based on the generated weights includes one or more of:
 performing a greater number of training iterations for a first transaction than for a second transaction based on the first transaction having a greater weight than the second transaction; or   performing a greater training adjustment for a first transaction than for a second transaction based on the first transaction having a greater weight than the second transaction.   
     
     
         14 . The non-transitory computer-readable medium of  claim 10 , wherein the generating the respective weights is based on relationships between labeled classifications and output predictions generated by the trained first classifier module for the transactions. 
     
     
         15 . The non-transitory computer-readable medium of  claim 10 , wherein the generating the respective weights provides greater weights for transactions whose output predictions are further from a labeled classification. 
     
     
         16 . A computer system, comprising:
 a processor;   a network interface; and   a non-transitory computer-readable medium having stored thereon instructions executable by the computer system to cause the computer system to perform operations comprising:   receiving, at a trained second classifier module of the computer system, an indication of a pending transaction initiated by a user computing device,   wherein a first classifier module was trained using first data for a set of completed transactions as training data input, wherein the first data includes, for respective transactions in the set of completed transactions both pre-transaction information and post-transaction information, wherein labeled classifications for transactions in the set of completed transactions are known, and wherein the post-transaction information for a first transaction in the set of completed transactions includes information for one or more transactions performed subsequent to a completion of the first transaction and non-transaction activity occurring subsequent to the completion of the first transaction for a user associated with the first transaction,   wherein the trained second classifier module was trained using operations comprising:
 generating respective weights for multiple transactions in the set of completed transactions based on classification outputs of the trained first classifier for the multiple transactions; and 
 training, based on the generated weights, a second classifier module using second data for the set of completed transactions as training data input, wherein the second data for the set of completed transactions includes pre-transaction information for transactions in the set of completed transactions; 
   classifying, using the trained second classifier module of the computer system, the pending transaction based on pre-transaction information for the pending transaction; and   the computer system generating, based on a classification output by the trained second classifier for the pending transaction, an authorization decision for the pending transaction, wherein the authorization decision specifies an indication of approval or non-approval for the pending transaction.   
     
     
         17 . The computer system of  claim 16 , wherein the training the second classifier module based on the generated weights is performed using gradient boosting tree machine learning techniques. 
     
     
         18 . The computer system of  claim 16 , wherein the training based on the generated weights includes one or more of:
 performing a greater number of training iterations for a first transaction than for a second transaction based on the first transaction having a greater weight than the second transaction; or   performing a greater training adjustment for a first transaction than for a second transaction based on the first transaction having a greater weight than the second transaction.   
     
     
         19 . The computer system of  claim 16 , wherein the generating the respective weights is based on relationships between labeled classifications and output predictions generated by the trained first classifier module for the transactions. 
     
     
         20 . The computer system of  claim 16 , wherein the generating the respective weights provides greater weights for transactions whose output predictions are further from a labeled classification. 
     
     
         21 . The computer system of  claim 16 , wherein the post-transaction information includes textual data input by a plurality of users associated with the post-transaction information.