US 11,922,469 B2
Automated news ranking and recommendation system
Lisa Kim, Jersey City, NY (US); Zhiqiang Ma, New York, NY (US); Grace Bang, New York, NY (US); Chong Wang, Queens, NY (US); Himani Singh, Brooklyn, NY (US); Russell Kociuba, Rochester, NY (US); Steven Pomerville, Rochester, NY (US); and Xiaomo Liu, Queens, NY (US)
Assigned to S&P Global Inc., New York, NY (US)
Filed by S&P Global Inc., New York, NY (US)
Filed on Apr. 1, 2022, as Appl. No. 17/657,709.
Application 17/657,709 is a continuation of application No. 16/779,434, filed on Jan. 31, 2020, granted, now 11,334,949.
Claims priority of provisional application 62/913,885, filed on Oct. 11, 2019.
Prior Publication US 2022/0230253 A1, Jul. 21, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 30/0282 (2023.01); G06F 16/2455 (2019.01); G06F 16/2457 (2019.01); G06F 16/28 (2019.01); G06F 16/35 (2019.01); G06F 16/9032 (2019.01); G06F 16/9038 (2019.01); G06F 16/906 (2019.01); G06F 16/951 (2019.01); G06F 16/9535 (2019.01); G06F 40/205 (2020.01); G06F 40/289 (2020.01); G06F 40/295 (2020.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G06Q 20/12 (2012.01); G06Q 40/06 (2012.01); G06V 30/416 (2022.01); H04L 67/01 (2022.01)
CPC G06Q 30/0282 (2013.01) [G06F 16/24556 (2019.01); G06F 16/24578 (2019.01); G06F 16/287 (2019.01); G06F 16/35 (2019.01); G06F 16/90332 (2019.01); G06F 16/9038 (2019.01); G06F 16/906 (2019.01); G06F 16/951 (2019.01); G06F 16/9535 (2019.01); G06F 40/205 (2020.01); G06F 40/289 (2020.01); G06F 40/295 (2020.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06Q 20/127 (2013.01); G06Q 40/06 (2013.01); G06V 30/416 (2022.01); H04L 67/01 (2022.05)] 19 Claims
OG exemplary drawing
 
1. A computer implemented method for recommending news articles, the computer implemented method comprising:
ingesting, by a computer system, the news articles from a plurality of news sources;
extracting, by the computer system, named entities from each news article to generate a one-hot vector for each news article using a statistical model;
clustering, by the computer system, the news articles into clusters based on the one-hot vectors for the news articles;
selecting, by the computer system, a representative news article for each cluster in the clusters;
converting, by the computer system using a machine learning model, each word of each representative news article into a word representation based on character embeddings;
modeling, by the computer system using the machine learning model, characteristics of use and characteristics of use across linguistic context for each word of each representative news article:
inputting, by the computer system, word representations into a convolutional layer followed by a max-pool layer in the machine learning model to generate an input representation for each representative news article;
generating, by the computer system using the machine learning model, a sentence representation for each representative news article based on the input representations for each news article;
merging, by the computer system, clusters in the clusters based on semantic of each representative news article in each cluster to form merged clusters using the sentence representation for each representative news article;
generating, by the computer system, a set of ranked clusters using the merged clusters and the sentence representations of each news article;
digitally displaying, by the computer system, the set of ranked clusters in a graphical user interface; and
manipulating, by the computer system, a number of controls in the graphical user interface to perform an action to the set of ranked clusters on the graphical user interface.