US 10,891,322 B2
Automatic conversation creator for news
Ming Zhou, Redmond, WA (US); Yu-Ting Kuo, Redmond, WA (US); Furu Wei, Redmond, WA (US); Lei Cui, Redmond, WA (US); Shaohan Huang, Redmond, WA (US); Kati London, Redmond, WA (US); Wei-Ying Ma, Redmond, WA (US); and Haoyong Zhang, Redmond, WA (US)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Appl. No. 15/772,472
PCT Filed Oct. 26, 2016, PCT No. PCT/US2016/058810
§ 371(c)(1), (2) Date Apr. 30, 2018,
PCT Pub. No. WO2017/075017, PCT Pub. Date May 4, 2017.
Claims priority of application No. 2015 1 0728069 (CN), filed on Oct. 30, 2015.
Prior Publication US 2018/0322188 A1, Nov. 8, 2018
Int. Cl. G06F 16/332 (2019.01); G06F 16/335 (2019.01); G06F 16/33 (2019.01); G06F 16/9535 (2019.01); G06Q 50/00 (2012.01); G06F 40/284 (2020.01); G06K 9/62 (2006.01)
CPC G06F 16/3329 (2019.01) [G06F 16/335 (2019.01); G06F 16/3347 (2019.01); G06F 16/9535 (2019.01); G06F 40/284 (2020.01); G06K 9/6269 (2013.01); G06Q 50/01 (2013.01)] 20 Claims
OG exemplary drawing
1. A computing device comprising:
a processor;
a display device; and
a computer-readable storage medium in communication with the processor, the computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by the processor, cause the computing device to:
identify lexical-level features for a plurality of pairs of comments and replies previously posted to one or more content item webpages, the lexical-level features including identifying a cosine similarity between two vectors, a first of the two vectors representing a comment of one of the plurality of pairs of comments and replies and a second of the two vectors representing a reply of the one of the plurality of pairs of comments and replies, the two vectors associated with a term frequency of a term in the one of the pair of the plurality of replies and comments;
receive information associated with a content item produced by a source system, the content item being accessible to other computing devices via a network;
in response to receiving the information, identify a start comment;
in response to identifying the start comment, create a reply to the start comment using at least a ranking model, the ranking model created at least in part on the identified lexical-level features; and
output the start comment and the reply.