US 12,748,992 B1 · Hierarchical hidden Markov model-based recommendation system and method with combined taxonomy and intent state representation

Abstract

Certain aspects of the disclosure provide methods, systems, and apparatuses for providing taxonomy-boosted hierarchical hidden Markov modeling to enhance recommendation inferences and machine learning model training. A hierarchical structure of enterprise knowledge is constructed to provide searching knowledge in a desired granularity. A hidden state space may be defined for a hierarchical hidden Markov model, where each hidden state in the hidden state space is associated with a node in the hierarchical structure and an intent category obtained from observing the actions, events, and behavior of the user. Training a classification model may be provided to predict a probability of each hidden state in the hidden state space based on a user input. Training the hierarchical hidden Markov model may be provided based on a training data set labeled for each hidden state in a hidden state space.

Claims18

Dependent claims

A method, comprising:

receiving a user input associated with a user;
processing the user input with a trained classification model to generate a predicted hidden state, wherein:
the predicted hidden state is associated with a trained hierarchical hidden Markov model, and
the predicted hidden state is associated with a node in a taxonomy and an intent category;
determining a most probable next hidden state using the trained hierarchical hidden Markov model and based on the predicted hidden state;
generating a recommendation based on the most probable next hidden state;
and
providing the recommendation to the user,
wherein generating the recommendation based on the most probable next hidden state comprises:
providing the user input and the most probable next hidden state to a language model in a prompt requesting a suggestion;
and
receiving the recommendation from the language model.

9 more elements

A computing system comprising one or more processors and one or more memories storing computer-executable instructions that are executable by the one or more processors to cause the computing system to:

receive a user input associated with a user;
process the user input with a trained classification model to generate a predicted hidden state, wherein:
the predicted hidden state is associated with a trained hierarchical hidden Markov model, and
the predicted hidden state is associated with a node in a taxonomy and an intent category;
determine a most probable next hidden state using the trained hierarchical hidden Markov model and based on the predicted hidden state;
generate a recommendation based on the most probable next hidden state;
and
provide the recommendation to the user,
wherein to cause the computing system to generate the recommendation based on the most probable next hidden state, the computer-executable instructions are executable by the one or more processors to cause the computing system to:
provide the user input and the most probable next hidden state to a language model in a prompt requesting a suggestion;
and
receive the recommendation from the language model.

9 more elements

Dependencies are inferred from the claim text.

Hierarchical hidden Markov model-based recommendation system and method with combined taxonomy and intent state representation

Life of this patent

Hierarchical hidden Markov model-based recommendation system and method with combined taxonomy and intent state representation

Life of this patent

Abstract

Claims18