US 11,726,892 B2
Realtime data stream cluster summarization and labeling system
Steve Weissinger, San Jose, CA (US); Luis Stevens, San Jose, CA (US); and Vincent Schiavone, San Jose, CA (US)
Assigned to Target Brands, Inc., Minneapolis, MN (US)
Filed by Target Brands, Inc., Minneapolis, MN (US)
Filed on Mar. 29, 2021, as Appl. No. 17/216,240.
Application 17/216,240 is a continuation of application No. 16/274,116, filed on Feb. 12, 2019, granted, now 10,963,360.
Application 16/274,116 is a continuation of application No. 15/530,187, filed on Dec. 8, 2016, granted, now 10,204,026, issued on Feb. 12, 2019.
Application 15/530,187 is a continuation in part of application No. 14/688,865, filed on Apr. 16, 2015, granted, now 10,599,697, issued on Mar. 24, 2020.
Application 14/688,865 is a continuation in part of application No. 14/214,410, filed on Mar. 14, 2014, granted, now 9,477,733, issued on Oct. 25, 2016.
Claims priority of provisional application 62/264,845, filed on Dec. 8, 2015.
Claims priority of provisional application 61/980,525, filed on Apr. 16, 2014.
Claims priority of provisional application 61/802,353, filed on Mar. 15, 2013.
Prior Publication US 2021/0357303 A1, Nov. 18, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 16/35 (2019.01); G06F 16/31 (2019.01); G06F 11/34 (2006.01); G06Q 30/0201 (2023.01); G06Q 50/00 (2012.01); G06F 16/901 (2019.01); G06F 16/9535 (2019.01); G06F 16/2455 (2019.01); H04L 65/60 (2022.01)
CPC G06F 11/3409 (2013.01) [G06F 16/24568 (2019.01); G06F 16/313 (2019.01); G06F 16/35 (2019.01); G06F 16/9024 (2019.01); G06F 16/9535 (2019.01); G06Q 30/0201 (2013.01); G06Q 50/01 (2013.01); H04L 65/60 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
at a computer system having real-time access to a data stream including a plurality of electronic communications, the computer system including at least one processor communicatively connected to a memory, executing instructions to cause the computer system to perform:
receiving a collection of topics, associated topic word probabilities in conjunction with a statistical topic model, and a set of documents associated with the collection of topics;
truncating the set of documents to form a truncated document set that includes documents having an aggregate topic word probability that meets truncation criteria;
determining, for each document in the truncated document set for a given topic, an aggregate topic word probability;
for one or more topic words in the truncated document set, identifying topic fragments including the one or more topic words and one or more non-stopwords, wherein identifying the topic fragments includes iterating from the one or more topic words and storing stopwords positioned relative to the one or more topic words until respective non-stopwords are identified; and
generating a topic label for the truncated document set including one or more of the identified topic fragments.