A system and method for adaptive cloud conversation platform

Abstract

An adaptive cloud conversation platform capable of making automated decisions regarding when and how to establish on-going communications with consumers so as to maximize the relationship between the consumer and a given brand. The system has a connection management services layer which determines what communications should be established and how they should be established, an initiation management services layer which determines when communications should be established, and a user management services layer which stores information about consumers and brands for determination of when and how communications should be established. Certain of these services have machine learning algorithms incorporated into them trained to perform analyses of the particular type of operation handled by that service. The outputs of each service can be used as inputs to other services, such that a network of machine learnings algorithms is created which determines when and how to establish on-going communications with consumers.

What is claimed is: 
     
         1 . An adaptive cloud conversation platform, comprising:
 a computing device comprising a memory, a processor, and a non-volatile data storage device;   a consumer profile database stored on the non-volatile data storage device, the consumer profile database comprising one or more consumer profiles;   a survey manager comprising a first plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to:
 receive conversation data for a conversation with a consumer, the conversation data comprising an interaction between the consumer and a brand and satisfaction data for the conversation; 
 process the conversation data through a first machine learning algorithm to obtain a survey strategy, the survey strategy comprising a determination that a survey of the consumer should be conducted and a type of survey to be conducted; and 
 forward the survey strategy to a conversation manager; 
   the conversation manager comprising a second plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to:
 receive the survey strategy; 
 retrieve a consumer profile for the consumer from the consumer profile database, the consumer profile comprising a plurality of preferences of the consumer; 
 process the plurality of preferences through a second machine learning algorithm to select a channel through which to conduct the survey with the consumer; and 
 forward the channel selection to a schedule manager; 
   the schedule manager comprising a third plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to:
 receive the channel selection from the conversation manager; 
 retrieve the consumer profile; 
 process the plurality of preferences through a third machine learning algorithm to select a time at which to conduct the callback with the consumer through the selected channel; 
 schedule a survey to be conducted with the consumer at the selected time through the selected channel; and forward the survey schedule to a callback manager; and 
   the callback manager comprising a fourth plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to:
 receive the survey schedule; and 
 retrieve a survey of the type specified in the survey strategy; 
 execute the survey strategy by conducting the survey at the selected time through the selected channel as indicated in the survey schedule. 
   
     
     
         2 . The platform of  claim 1 , wherein:
 the survey manager is further configured to:
 receive survey feedback from the consumer; 
 process the survey feedback through the first machine learning algorithm to determine whether a callback is recommended based on the survey feedback; and 
 forward the determination to the conversation manager; 
   the conversation manager is further configured to:
 receive the determination; 
 retrieve the consumer profile; 
 process the plurality of preferences through the second machine learning algorithm to select a channel through which to conduct the callback with the consumer; 
   the schedule manager is further configured to:
 receive the channel selection from the conversation manager; 
 retrieve the consumer profile; 
 process the plurality of preferences through the third machine learning algorithm to select a time at which to conduct the callback with the consumer through the selected channel; 
 schedule a callback to be conducted with the consumer at the selected time through the selected channel; and 
 forward the survey schedule to the callback manager; and 
   the callback manager is further configured to:
 receive the callback schedule; and 
 execute the callback by conducting the callback at the selected time through the selected channel as indicated in the callback schedule. 
   
     
     
         3 . The platform of  claim 1 , further comprising a consumer context manager comprising a third plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to:
 receive the conversation data;   retrieve the consumer profile, the consumer profile further comprising a plurality of behaviors of the consumer;   process the conversation data and the plurality of behaviors of the consumer through a fourth machine learning algorithm to determine whether a second callback to the consumer should be made; and   where the determination is that a second callback should be made, forward the determination to the conversation manager as the determination that a callback should be made to a consumer.   
     
     
         4 . The platform of  claim 3 , further comprising a session manager comprising a fourth plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to:
 receive the conversation data;   process the conversation data through a fifth machine learning algorithm to determine a consumer sentiment; and   forward the determined sentiment to the consumer context manager as an additional input to the fourth machine learning algorithm's determination as to whether a callback to the consumer should be made.   
     
     
         5 . The platform of  claim 3 , wherein:
 the conversation manager is further configured to:
 process the conversation data through a sixth machine learning algorithm to determine a consumer goal, need, or intent; and 
 forward the determined goal, need, or intent to the consumer context manager as an additional input to the fourth machine learning algorithm's determination as to whether a callback to the consumer should be made. 
   
     
     
         6 . The platform of  claim 3 , further comprising:
 an event rules database stored on the non-volatile data storage device, the event rules database comprising rules for triggering communications with consumers based on events occurring outside of a conversation; and   an event manager comprising a seventh plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to:
 receive notification of an event; 
 match the event to a rule in the event rules database; and 
 forward the matched event to the conversation manager as the determination that a callback should be made to the consumer. 
   
     
     
         7 . The platform of  claim 6 , wherein:
 the event manager is further configured to:
 retrieve the consumer profile; 
 process the consumer profile and one or more rules from the event rules database through an eighth machine learning algorithm to determine a new rule for triggering communications with the consumer; and 
 store the new rule in the event rules database. 
   
     
     
         8 . The platform of  claim 3 , further comprising:
 a brand environment database stored on the non-volatile data storage device, the brand environment database comprising brand information related to conversations with consumers of the brand;   an environment manager comprising an eighth plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to:
 retrieve the consumer profile; 
 retrieve the brand information from the brand environment database; 
 process the consumer profile and the brand information through a ninth machine learning algorithm to determine whether a campaign of communications should be established with a plurality of consumers; and 
 forward the determination to the conversation manager as the determination that a callback should be made to the consumer. 
   
     
     
         9 . The platform of  claim 8 , further comprising:
 a consumer manager comprising a ninth plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to:
 retrieve the consumer profile for the consumer from the consumer profile database; 
 receive a fitness parameter from the environment manager; 
 process the consumer profile and the fitness parameter through a tenth machine learning algorithm to identify opportunities for proactive conversations with the consumer; and 
 forward the identified to the consumer context manager as an additional input to the second machine learning algorithm's selection of the channel through which the callback should be made. 
   
     
     
         10 . A method for operating an adaptive cloud conversation platform, comprising the steps of:
 using a survey manager operating on a computing device comprising a memory, a processor, and a non-volatile data storage device to:
 receive conversation data for a conversation with a consumer, the conversation data comprising an interaction between the consumer and a brand and satisfaction data for the conversation; 
 process the conversation data through a first machine learning algorithm to obtain a survey strategy, the survey strategy comprising a determination that a survey of the consumer should be conducted and a type of survey to be conducted; and 
 forward the survey strategy to a conversation manager; 
   a conversation manager comprising a second plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to:
 receive the survey strategy; 
 retrieve a consumer profile for the consumer from a consumer profile database stored on the non-volatile data storage device, the consumer profile database comprising one or more consumer profiles, and the consumer profile comprising a plurality of preferences of the consumer; 
 process the plurality of preferences through a second machine learning algorithm to select a channel through which to conduct the survey with the consumer; and 
 forward the channel selection to a schedule manager; 
   using a schedule manager operating on the computing device to:
 receive the channel selection from the conversation manager; 
 retrieve the consumer profile; 
 process the plurality of preferences through a third machine learning algorithm to select a time at which to conduct the callback with the consumer through the selected channel; 
 schedule a survey to be conducted with the consumer at the selected time through the selected channel; and forward the survey schedule to a callback manager; and 
   using a callback manager operating on the computing device to:
 receive the survey schedule; and 
 retrieve a survey of the type specified in the survey strategy; 
 execute the survey strategy by conducting the survey at the selected time through the selected channel as indicated in the survey schedule. 
   
     
     
         11 . The method of  claim 10 , further comprising the steps of:
 using the survey manager to:
 receive survey feedback from the consumer; 
 process the survey feedback through the first machine learning algorithm to determine whether a callback is recommended based on the survey feedback; and 
 forward the determination to the conversation manager; 
   using the conversation manager to:
 receive the determination; 
 retrieve the consumer profile; 
 process the plurality of preferences through the second machine learning algorithm to select a channel through which to conduct the callback with the consumer; 
   using the schedule manager to:
 receive the channel selection from the conversation manager; 
 retrieve the consumer profile; 
 process the plurality of preferences through the third machine learning algorithm to select a time at which to conduct the callback with the consumer through the selected channel; 
 schedule a callback to be conducted with the consumer at the selected time through the selected channel; and 
 forward the survey schedule to the callback manager; and 
   using the callback manager to:
 receive the callback schedule; and 
 execute the callback by conducting the callback at the selected time through the selected channel as indicated in the callback schedule. 
   
     
     
         12 . The method of  claim 10 , further comprising the steps of using a consumer context manager operating on the computing device to:
 receive the conversation data;   retrieve the consumer profile, the consumer profile further comprising a plurality of behaviors of the consumer;   process the conversation data and the plurality of behaviors of the consumer through a fourth machine learning algorithm to determine whether a second callback to the consumer should be made; and   where the determination is that a second callback should be made, forward the determination to the conversation manager as the determination that a callback should be made to a consumer.   
     
     
         13 . The method of  claim 12 , further comprising the steps of using a session manager operating on the computing device to:
 receive the conversation data;   process the conversation data through a fifth machine learning algorithm to determine a consumer sentiment; and   forward the determined sentiment to the consumer context manager as an additional input to the fourth machine learning algorithm's determination as to whether a callback to the consumer should be made.   
     
     
         14 . The method of  claim 12 , further comprising the steps of:
 using the conversation manager to:
 process the conversation data through a sixth machine learning algorithm to determine a consumer goal, need, or intent; and 
 forward the determined goal, need, or intent to the consumer context manager as an additional input to the fourth machine learning algorithm's determination as to whether a callback to the consumer should be made. 
   
     
     
         15 . The method of  claim 12 , further comprising the steps of:
 creating an event rules database on the non-volatile data storage device, the event rules database comprising rules for triggering communications with consumers based on events occurring outside of a conversation; and   using an event manager operating on the computing device to:
 receive notification of an event; 
 match the event to a rule in the event rules database; and 
 forward the matched event to the conversation manager as the determination that a callback should be made to the consumer. 
   
     
     
         16 . The method of  claim 15 , further comprising the steps of:
 using the event manager to:
 retrieve the consumer profile; 
 process the consumer profile and one or more rules from the event rules database through an eighth machine learning algorithm to determine a new rule for triggering communications with the consumer; and 
 store the new rule in the event rules database. 
   
     
     
         17 . The method of  claim 12 , further comprising the steps of:
 creating a brand environment database on the non-volatile data storage device, the brand environment database comprising brand information related to conversations with consumers of the brand;   using an environment manager operating on the computing device to:
 retrieve the consumer profile; 
 retrieve the brand information from the brand environment database; 
 process the consumer profile and the brand information through a ninth machine learning algorithm to determine whether a campaign of communications should be established with a plurality of consumers; and 
 forward the determination to the conversation manager as the determination that a callback should be made to the consumer. 
   
     
     
         18 . The method of  claim 17 , further comprising the steps of:
 using a consumer manager operating on the computing device to:
 retrieve the consumer profile for the consumer from the consumer profile database; 
 receive a fitness parameter from the environment manager; 
 process the consumer profile and the fitness parameter through a tenth machine learning algorithm to identify opportunities for proactive conversations with the consumer; and 
 forward the identified to the consumer context manager as an additional input to the second machine learning algorithm's selection of the channel through which the callback should be made.