US 12,406,010 B2
Machine-learned classification of network traffic
Charles Ronald Allieri, Wellesley, MA (US); Marco Lagi, Medford, MA (US); Vicent Alabau, Alboraya (ES); Enrique Pons, Alboraya (ES); Ihab Khoury, Alboraya (ES); and Caleb Castleberry, Dawsonville, GA (US)
Assigned to Intentsify, LLC, Westwood, MA (US)
Filed by Intentsify, LLC, Westwood, MA (US)
Filed on Jun. 25, 2024, as Appl. No. 18/753,604.
Application 18/753,604 is a division of application No. 18/723,793, previously published as PCT/IB2023/062101, filed on Nov. 30, 2023.
Claims priority of provisional application 63/581,491, filed on Sep. 8, 2023.
Claims priority of provisional application 63/385,614, filed on Nov. 30, 2022.
Prior Publication US 2024/0346093 A1, Oct. 17, 2024
Int. Cl. G06F 16/00 (2019.01); G06F 16/951 (2019.01); G06F 16/955 (2019.01); G06F 16/958 (2019.01); G06F 18/2415 (2023.01); G06F 40/279 (2020.01)
CPC G06F 16/951 (2019.01) [G06F 16/9566 (2019.01); G06F 16/958 (2019.01); G06F 18/2415 (2023.01); G06F 40/279 (2020.01)] 13 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving target input data that includes keyword input data and topic input data, wherein:
the target input data is based on text specified by a user,
the keyword input data includes user-specified keywords to locate in webpages, and
the topic input data includes user-specified topics to locate in webpages;
accessing processed network data, wherein:
the processed network data includes a plurality of groups,
each group of the plurality of groups includes a plurality of URL data objects, and
each group of the plurality of groups is associated with an entity;
generating a dynamic intent score for each group of the plurality of groups by:
for each URL data object of the plurality of URL data objects of the group:
extracting keywords from a webpage associated with the URL data object that are similar to keywords of the target input data;
comparing the extracted keywords with the keywords of the target input data;
generating a keyword comparison value for the URL data object based on the comparison;
creating target input data embeddings based on the topic input data:
scraping the webpage associated with the URL data object to generate a scraped text data object;
creating web embeddings by providing the scraped text data object to a machine learning module; and
generating a topic comparison value by comparing the web embeddings with the target input data embeddings;
generating a total comparison value for the URL data object by combining (i) the keyword comparison value and (ii) the topic comparison value; and
generating the dynamic intent score based on the total comparison values of the plurality of URL data objects of the group;
ranking the plurality of groups according to their respective dynamic intent scores;
selecting a subset of the plurality of groups according to the ranking; and
generating a target account list including the entities associated with the subset of the plurality of groups.