Dynamically determining real-time offers

Abstract

Disclosed are systems and techniques for dynamically providing real-time offers. For instance, user eligibility for one or more offers can be determined based on a plurality of dynamic user attributes associated with the user and one or more data sets corresponding to similarly situated users. An input can be received corresponding to an acceptance by the user of at least one offer selected from the one or more offers. In response to receiving the input, an updated plurality of the dynamic user attributes can be retrieved, wherein the dynamic user attributes are constantly updated in real time based on user activity. Based on the updated plurality of dynamic user attributes and the one or more data sets corresponding to the similarly situated user, it can be determined whether the user remains eligible for the at least one offer. An offer confirmation can be sent upon confirming eligibility.

1 . (canceled) 
     
     
         2 . A computer-implemented method comprising:
 receiving in real-time a set of dynamic user attributes associated with a user, wherein the set of dynamic user attributes is continuously updated in real-time based on user activity;   dynamically training a machine learning model to generate real-time offers, wherein the machine learning model is dynamically trained using a data set of sample user attributes associated with a set of sample users and sample real-time offers corresponding to the set of sample users;   repeatedly generating in real-time new offers for the user, wherein the new offers are generated by the machine learning model as the set of dynamic user attributes are continuously updated in real-time;   providing the new offers as the new offers are repeatedly generated by the machine learning model, wherein when the new offers are received by a user device associated with the user, the user device presents the new offers; and   modifying the machine learning model in real-time, wherein the machine learning model is modified based on the new offers and the set of dynamic user attributes as the set of dynamic user attributes are continuously updated.   
     
     
         3 . The computer-implemented method of  claim 2 , further comprising:
 receiving an input corresponding to acceptance of an offer from the new offers; and   providing an offer confirmation, wherein the offer confirmation is provided as a result of the user remaining qualified for the offer based on the set of dynamic user attributes.   
     
     
         4 . The computer-implemented method of  claim 2 , further comprising:
 determining that the user is disqualified from one or more offers from the new offers, wherein the user is disqualified from the one or more offers as the set of dynamic user attributes are continuously updated; and   removing the one or more offers.   
     
     
         5 . The computer-implemented method of  claim 2 , wherein the set of dynamic user attributes are received in response to an input indicating rejection of an initial offer. 
     
     
         6 . The computer-implemented method of  claim 2 , further comprising:
 calculating one or more probabilities associated with the new offers, wherein the one or more probabilities correspond to a determination of eligibility for the new offers, and wherein the new offers are provided based on the determination.   
     
     
         7 . The computer-implemented method of  claim 2 , wherein the new offers include a temporal threshold, and wherein the temporal threshold corresponds to a pre-determined period of time after which the new offers are expired. 
     
     
         8 . The computer-implemented method of  claim 2 , wherein the set of dynamic user attributes includes at least one of a credit score, credit history, spending patterns, and user preferences. 
     
     
         9 . A system, comprising:
 one or more processors; and   memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to:
 receive in real-time a set of dynamic user attributes associated with a user, wherein the set of dynamic user attributes is continuously updated in real-time based on user activity; 
 dynamically train a machine learning model to generate real-time offers, wherein the machine learning model is dynamically trained using a data set of sample user attributes associated with a set of sample users and sample real-time offers corresponding to the set of sample users; 
 repeatedly generate in real-time new offers for the user, wherein the new offers are generated by the machine learning model as the set of dynamic user attributes are continuously updated in real-time; 
 provide the new offers as the new offers are repeatedly generated by the machine learning model, wherein when the new offers are received by a user device associated with the user, the user device presents the new offers; and 
 modify the machine learning model in real-time, wherein the machine learning model is modified based on the new offers and the set of dynamic user attributes as the set of dynamic user attributes are continuously updated. 
   
     
     
         10 . The system of  claim 9 , wherein the instructions further cause the system to:
 receive an input corresponding to acceptance of an offer from the new offers; and   provide an offer confirmation, wherein the offer confirmation is provided as a result of the user remaining qualified for the offer based on the set of dynamic user attributes.   
     
     
         11 . The system of  claim 9 , wherein the instructions further cause the system to:
 determine that the user is disqualified from one or more offers from the new offers, wherein the user is disqualified from the one or more offers as the set of dynamic user attributes are continuously updated; and   remove the one or more offers.   
     
     
         12 . The system of  claim 9 , wherein the set of dynamic user attributes are received in response to an input indicating rejection of an initial offer. 
     
     
         13 . The system of  claim 9 , wherein the instructions further cause the system to:
 calculate one or more probabilities associated with the new offers, wherein the one or more probabilities correspond to a determination of eligibility for the new offers, and wherein the new offers are provided based on the determination.   
     
     
         14 . The system of  claim 9 , wherein the new offers include a temporal threshold, and wherein the temporal threshold corresponds to a pre-determined period of time after which the new offers are expired. 
     
     
         15 . The system of  claim 9 , wherein the set of dynamic user attributes includes at least one of a credit score, credit history, spending patterns, and user preferences. 
     
     
         16 . A non-transitory, computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to:
 receive in real-time a set of dynamic user attributes associated with a user, wherein the set of dynamic user attributes is continuously updated in real-time based on user activity;   dynamically train a machine learning model to generate real-time offers, wherein the machine learning model is dynamically trained using a data set of sample user attributes associated with a set of sample users and sample real-time offers corresponding to the set of sample users;   repeatedly generate in real-time new offers for the user, wherein the new offers are generated by the machine learning model as the set of dynamic user attributes are continuously updated in real-time;   provide the new offers as the new offers are repeatedly generated by the machine learning model, wherein when the new offers are received by a user device associated with the user, the user device presents the new offers; and   modify the machine learning model in real-time, wherein the machine learning model is modified based on the new offers and the set of dynamic user attributes as the set of dynamic user attributes are continuously updated.   
     
     
         17 . The non-transitory, computer-readable storage medium of  claim 16 , wherein the executable instructions further cause the computer system to:
 receive an input corresponding to acceptance of an offer from the new offers; and   provide an offer confirmation, wherein the offer confirmation is provided as a result of the user remaining qualified for the offer based on the set of dynamic user attributes.   
     
     
         18 . The non-transitory, computer-readable storage medium of  claim 16 , wherein the executable instructions further cause the computer system to:
 determine that the user is disqualified from one or more offers from the new offers, wherein the user is disqualified from the one or more offers as the set of dynamic user attributes are continuously updated; and   remove the one or more offers.   
     
     
         19 . The non-transitory, computer-readable storage medium of  claim 16 , wherein the set of dynamic user attributes are received in response to an input indicating rejection of an initial offer. 
     
     
         20 . The non-transitory, computer-readable storage medium of  claim 16 , wherein the executable instructions further cause the computer system to:
 calculate one or more probabilities associated with the new offers, wherein the one or more probabilities correspond to a determination of eligibility for the new offers, and wherein the new offers are provided based on the determination.   
     
     
         21 . The non-transitory, computer-readable storage medium of  claim 16 , wherein the new offers include a temporal threshold, and wherein the temporal threshold corresponds to a pre-determined period of time after which the new offers are expired. 
     
     
         22 . The non-transitory, computer-readable storage medium of  claim 16 , wherein the set of dynamic user attributes includes at least one of a credit score, credit history, spending patterns, and user preferences.