System and method for implementing an artificially intelligent virtual assistant using machine learning
First Claim
1. A machine learning-based system that implements an artificially intelligent virtual agent the system comprising:
- a cache memory storing at least one historical user query posed by a given user to the machine learning-based system at a point earner in time;
a user interface that receives a real-time query from the given user;
one or more networked computing devices that implement the machine learning-based system including;
a trained machine learning model that performs;
(a) a first classification of the real-time query by the given user, the first classification includes an output of a competency classification label identifying at least one area of aptitude required for generating a response to the given user based on the real-time query;
(b) contemporaneous with the first classification, a second classification of the real-time query by the given user that is distinct from the first classification indicating whether the real-time user query comprises a follow-on query relative to the at least one historical user query of the given user that is stored in the cache memory;
wherein;
based on the second classification of the real-time query that includes an output of a follow-on query classification label by the trained machine learning model, the machine learning-based system automatically performs;
(1) a search of the cache memory, the search comprising at least data relating to the competency classification label output from the first classification and the follow-on query classification label of the second classification;
(2) an identification from within the cache memory of at least one historical query of the given user based on the search;
based on the identification of the at least one historical query of the given user, the machine learning-based system;
retrieves, from a memory device, a historical response to the at least one historical query of the given user;
the machine learning-based system further;
generates one or more new slot values based on the real-time query of the given user;
updates one or more historical slot values of the historical response to the at least one historical query of the given user with the one or more new slot values;
outputs a communication hi response to the real-time query of the given user, the communication comprising the historical response updated with the one or more new slot values.
1 Assignment
0 Petitions
Accused Products
Abstract
Systems and methods for implementing an artificially intelligent virtual assistant includes collecting a user query; using a competency classification machine learning model to generate a competency label for the user query; using a slot identification machine learning model to segment the text of the query and label each of the slots of the query; generating a slot value for each of the slots of the query; generating a handler for each of the slot values; and using the slot values to: identify an external data source relevant to the user query, fetch user data from the external data source, and apply one or more operations to the query to generate response data; and using the response data, to generate a response to the user query.
-
Citations
10 Claims
-
1. A machine learning-based system that implements an artificially intelligent virtual agent the system comprising:
-
a cache memory storing at least one historical user query posed by a given user to the machine learning-based system at a point earner in time; a user interface that receives a real-time query from the given user; one or more networked computing devices that implement the machine learning-based system including; a trained machine learning model that performs; (a) a first classification of the real-time query by the given user, the first classification includes an output of a competency classification label identifying at least one area of aptitude required for generating a response to the given user based on the real-time query; (b) contemporaneous with the first classification, a second classification of the real-time query by the given user that is distinct from the first classification indicating whether the real-time user query comprises a follow-on query relative to the at least one historical user query of the given user that is stored in the cache memory; wherein; based on the second classification of the real-time query that includes an output of a follow-on query classification label by the trained machine learning model, the machine learning-based system automatically performs; (1) a search of the cache memory, the search comprising at least data relating to the competency classification label output from the first classification and the follow-on query classification label of the second classification; (2) an identification from within the cache memory of at least one historical query of the given user based on the search; based on the identification of the at least one historical query of the given user, the machine learning-based system; retrieves, from a memory device, a historical response to the at least one historical query of the given user; the machine learning-based system further; generates one or more new slot values based on the real-time query of the given user; updates one or more historical slot values of the historical response to the at least one historical query of the given user with the one or more new slot values; outputs a communication hi response to the real-time query of the given user, the communication comprising the historical response updated with the one or more new slot values. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
-
Specification