Integrated Assistance Platform

Abstract

Systems and methods disclosed herein relate to autonomous agents. A first autonomous agent receives, from a first sensor, a first set of event data indicating events relating to a subject. The first autonomous agent provides the first set of event data to a data aggregator. The first autonomous agent receives, from the data aggregator, correlated event data including events sensed by the first autonomous agent and a second autonomous agent. The first autonomous agent applies machine learning model to the correlated event data to predict a first pattern of activity and determines, based on the first pattern of activity, that a first action is to be performed, causing the first actuator module to perform the first action.

What is claimed is: 
     
         1 . A device comprising:
 a processing system including a processor; and   a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:   receiving, at a first autonomous agent of a plurality of autonomous agents, a first set of event data indicating events relating to a subject, wherein each of the plurality of autonomous agents includes a respective machine learning model;   providing the first set of event data to a data aggregator that also receives a second set of event data relating to the subject from a second autonomous agent of the plurality of autonomous agents,   receiving, from the data aggregator, correlated event data comprising the first set of event data correlated with the second set of event data; and   predicting a first pattern of activity of the subject by applying a first machine learning model of the first autonomous agent to the correlated event data.   
     
     
         2 . The device of  claim 1 , wherein the operations further comprise:
 applying the first set of event data to the first machine learning model to update the first machine learning model.   
     
     
         3 . The device of  claim 1 , wherein the operations further comprise:
 applying the correlated event data to the first machine learning model to update the first machine learning model.   
     
     
         4 . The device of  claim 1 , wherein the operations further comprise:
 receiving, at the first autonomous agent, additional event data from an internet-based service; and   providing the additional event data to the data aggregator.   
     
     
         5 . The device of  claim 4 , wherein the correlated event received from data aggregator comprises the first set of event data correlated with the second set of event data and correlated with the additional event data. 
     
     
         6 . The device of  claim 1 , wherein the operations further comprise:
 receiving, at the first autonomous agent, a voice command;   applying the voice command to the first machine learning model to identify an action to be taken by the subject; and   providing instructions for the subject to take the action.   
     
     
         7 . The device of  claim 1 , wherein the providing the first set of event data to the data aggregator comprises providing the first set of event data to a second device that includes the data aggregator. 
     
     
         8 . The device of  claim 1 , wherein the first autonomous agent comprises a scheduling agent, wherein the first pattern of activity comprises scheduling an appointment. 
     
     
         9 . The device of  claim 1 , wherein the data aggregator correlates a plurality of events from the first set of event data and the second set of event data from the second autonomous agent. 
     
     
         10 . A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising:
 receiving, at a first autonomous agent of a plurality of autonomous agents, a first set of event data indicating events relating to a subject, wherein each of the plurality of autonomous agents includes a respective machine learning model;   providing the first set of event data to a data aggregator that also receives a second set of event data relating to the subject from a second autonomous agent of the plurality of autonomous agents,   receiving, from the data aggregator, correlated event data comprising the first set of event data correlated with the second set of event data; and   predicting a first pattern of activity of the subject by applying a first machine learning model of the first autonomous agent to the correlated event data.   
     
     
         11 . The non-transitory machine-readable medium of  claim 10 , further comprising:
 applying the first set of event data to the first machine learning model to update the first machine learning model.   
     
     
         12 . The non-transitory machine-readable medium of  claim 10 , further comprising:
 applying the correlated event data to the first machine learning model to update the first machine learning model.   
     
     
         13 . The non-transitory machine-readable medium of  claim 10 , further comprising:
 receiving, at the first autonomous agent, additional event data from an internet-based service; and   providing the additional event data to the data aggregator.   
     
     
         14 . The non-transitory machine-readable medium of  claim 13 , wherein the correlated event received from data aggregator comprises the first set of event data correlated with the second set of event data and correlated with the additional event data. 
     
     
         15 . The non-transitory machine-readable medium of  claim 10 , further comprising:
 receiving, at the first autonomous agent, a voice command;   applying the voice command to the first machine learning model to identify an action to be taken by the subject; and   providing instructions for the subject to take the action.   
     
     
         16 . The non-transitory machine-readable medium of  claim 10 , wherein the first autonomous agent comprises a scheduling agent, wherein the first pattern of activity comprises scheduling an appointment. 
     
     
         17 . A method comprising:
 receiving, by a processing system including a processor, at a first autonomous agent of a plurality of autonomous agents, a first set of event data indicating events relating to a subject, wherein each of the plurality of autonomous agents includes a respective machine learning model;   providing, by the processing system, the first set of event data to a data aggregator that also receives a second set of event data relating to the subject from a second autonomous agent of the plurality of autonomous agents,   receiving, by the processing system, from the data aggregator, correlated event data comprising the first set of event data correlated with the second set of event data; and   predicting, by the processing system, a first pattern of activity of the subject by applying a first machine learning model of the first autonomous agent to the correlated event data.   
     
     
         18 . The method of  claim 17 , further comprising:
 applying, by the processing system, the first set of event data to the first machine learning model to update the first machine learning model.   
     
     
         19 . The method of  claim 17 , further comprising:
 applying, by the processing system, the correlated event data to the first machine learning model to update the first machine learning model.   
     
     
         20 . The method of  claim 17 , further comprising:
 receiving, by the processing system, at the first autonomous agent, additional event data from an internet-based service; and   providing, by the processing system, the additional event data to the data aggregator.