Patent ID: 11880798
Assignee: CAPITAL ONE SERVICES, LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A system for improving document content, the system comprising:
one or more processors; and
a non-transitory computer-readable storage medium storing instructions, which when executed by the one or more processors cause the one or more processors to:
receive a document comprising a plurality of textual sections;
access a plurality of section templates corresponding to a plurality of predetermined sections, wherein each section template of the plurality of section templates comprises corresponding textual data, and wherein each section template is associated with a corresponding predetermined section template label of a plurality of predetermined section labels;
determine, using textual data within the plurality of section templates, a set of textual sections of the plurality of textual sections, such that each section of the set of textual sections matches a predetermined section identified by a predetermined section template label of the plurality of predetermined section labels; and
for a section in the set of textual sections:
generate a vector representation of the section based on section textual data within the section of the document;
input the vector representation of the section into a machine learning model corresponding to the section to obtain a score for the section of the document, wherein each section in the set of textual sections corresponds to a different machine learning model of a plurality of machine learning models, and wherein each machine learning model has been trained using training data associated with each corresponding to the section, and wherein output parameters of each machine learning model are automatically fed back into each corresponding machine learning model as input to further adjust weights or biases of each machine learning model;
in response to determining that the score satisfies a threshold, encoding section content of the section into a textual representation;
input the textual representation of the section into a decision tree corresponding to the section to obtain a set of top features for the section, wherein each section in the set of textual sections corresponds to a different decision tree of a plurality of decision trees;
in response to determining that one or more top features are missing from the section, generating one or more recommendations related to the one or more top features; and
provide, to a user, the one or more recommendations and the score.