US 12,321,701 B2
Building and using target-based sentiment models
Vishal Anand, Redmond, WA (US); Ananya Mishra, Seattle, WA (US); Pramodith Ballapuram, Dublin (IE); and Cheng Wu, Bellevue, WA (US)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Nov. 4, 2022, as Appl. No. 17/981,293.
Prior Publication US 2024/0152696 A1, May 9, 2024
Int. Cl. G06F 40/284 (2020.01); G06F 40/56 (2020.01); G06Q 30/0203 (2023.01); G06Q 30/0282 (2023.01)
CPC G06F 40/284 (2020.01) [G06F 40/56 (2020.01); G06Q 30/0203 (2013.01); G06Q 30/0282 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method comprising:
training a generative language model to identify features that it was not explicitly trained with, the training the generative language model comprising:
accessing training data and annotated training data, the annotated training data comprising annotations of the training data constrained by a predetermined template to be in a specific format; and
training, by a training component, the generative language model to perform natural language generation tasks that include generating one or more sentences comprising a sentiment, a target, and a reason for the sentiment in a format defined by the predetermined template without having been trained on the target or reason;
accessing, by a communication interface of a network system, customer data that includes a plurality of feedback input;
transmitting the customer data to an evaluation component of the network system;
generating, by the generative language model associated with the evaluation component, the one or more sentences based on a feedback input of the plurality of feedback inputs, the one or more sentences each including the sentiment, the target, and the reason for the sentiment in the format defined by the predetermined template used to train the generative language model;
identifying, by the evaluation component, the sentiment, the target, and the reason from a sentence of the one or more sentences generated by the generative language model; and
automatically, without human intervention, surfacing, by a recommendation module, a recommendation for an action to be performed by a user based on the sentiment and the reason and on a table that indicates a plurality of outputs from the generative language model, a sentiment associated with each output, one or more tokens associated with a target or reason associated with each output, and a recommendation for an action to be performed for each output.