US 12,244,556 B1
Classifying data using machine learning
Ying Jiang, Fremont, CA (US); Apoorv Sharma, San Mateo, CA (US); and Brian Matthew Holligan, San Francisco, CA (US)
Assigned to Doma Technology LLC, San Francisco, CA (US)
Filed by Doma Technology LLC, San Francisco, CA (US)
Filed on Feb. 21, 2023, as Appl. No. 18/112,442.
Claims priority of provisional application 63/312,790, filed on Feb. 22, 2022.
Int. Cl. G06F 15/16 (2006.01); G06F 40/205 (2020.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01); G06V 30/19 (2022.01); H04L 51/063 (2022.01); H04L 51/08 (2022.01); H04L 51/48 (2022.01)
CPC H04L 51/48 (2022.05) [G06F 40/205 (2020.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01); G06V 30/19173 (2022.01); H04L 51/063 (2013.01); H04L 51/08 (2013.01)] 25 Claims
OG exemplary drawing
 
1. A method comprising:
receiving a message;
parsing the message to identify individual sentences within the message;
encoding each sentence into a vector that represents a semantic meaning of the sentence;
applying one or more classification models to the message including: providing each vector to a machine learning model, wherein the machine learning model is trained to generate a prediction of one or more classifications for each sentence of the message, the classification for each sentence of the message indicating a prediction of whether the sentence is actionable or not, wherein a classification of actionable indicates that the sentence is associated with a process of a real estate transaction and includes content for advancing a workflow of the real estate transaction;
generating a predicted classification for the message as a whole as being actionable or unactionable based on the classifications of individual sentences of the message; and
determining a message routing based on the predicted classification.