US 12,260,611 B1
Systems and methods for contextual machine learning prompt generation
Elham Saraee, Medford, MA (US); Jehan Hamedi, Wellesley, MA (US); and Zachary Halloran, Franklin, MA (US)
Assigned to VIZIT LABS, INC., Boston, MA (US)
Filed by VIZIT LABS, INC., Boston, MA (US)
Filed on Nov. 14, 2024, as Appl. No. 18/948,435.
Int. Cl. G06F 16/438 (2019.01); G06N 3/045 (2023.01); G06V 10/40 (2022.01); G06V 10/74 (2022.01); G06V 10/82 (2022.01)
CPC G06V 10/761 (2022.01) [G06F 16/438 (2019.01); G06N 3/045 (2023.01); G06V 10/40 (2022.01); G06V 10/82 (2022.01)] 29 Claims
OG exemplary drawing
 
1. A method for training a text generation machine learning model for prompt generation, comprising:
receiving, by one or more processors, a plurality of training text strings;
executing, by the one or more processors, a text generation machine learning model using each of the plurality of training text strings to generate a plurality of training generated text strings, each of the plurality of generated text strings corresponding to generating a content item;
executing, by the one or more processors, a content scoring machine learning model using each of the plurality of generated text strings to generate a text performance score for each of the plurality of training generated text strings;
for each of the plurality of generated text strings, adjusting, by the one or more processors, one or more weights or parameters of the text generation machine learning model proportional to the text performance score for the generated text string according to a loss function.