IP Library › Granted Patent US 12,470,503
Granted Patent B2
US 12,470,503 · App. 17/856,752 · Granted Nov 11, 2025

Customized message suggestion with user embedding vectors

Inventors: Kelsey Taylor Ball (Jersey City, NJ); Tao Lei (Jersey City, NJ); Christopher David Fox (Mastic, NY); Joseph Ellsworth Hackman (New York, NY)
Assignee: ASAPP, INC.
H04L51/046G06F40/56G06N3/049
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Quick Facts
Patent No.
US 12,470,503
App. No.
17/856,752
Granted
Nov 11, 2025
Kind
B2
Abstract

A message may be suggested to a user participating in a conversation using one or more neural networks where the suggested message is adapted to the preferences or communication style of the user. The suggested message may be adapted to the user with a user embedding vector that represents the preferences or communication style of the user in a vector space. To suggest a message to the user, a conversation feature vector may be computed by processing the text of the conversation with a neural network. A context score may be computed for one or more designated messages, where the context score is computed by processing the user embedding vector, the conversation feature vector, and a designated message feature vector with a neural network. A designated message may be selected as a suggested message for the user using the context scores. The suggestion may then be presented to the user.

Claims (63)

1 . A computer-implemented method, comprising:

obtaining a user embedding vector corresponding to a user, wherein the user embedding vector represents the user in a first vector space;

receiving text of a conversation with the user;

computing a conversation feature vector using the text of the conversation and a first neural network, wherein the conversation feature vector represents the conversation in a second vector space;

obtaining a set of designated messages, wherein each designated message is associated with a corresponding designated message feature vector;

computing a first context score for a first designated message of the set of designated messages by processing the user embedding vector, the conversation feature vector, and a first designated message feature vector with a second neural network;

selecting the first designated message using the first context score; and

presenting the first designated message as a suggested message to the user.

2 . The computer-implemented method of claim 1 , wherein computing the conversation feature vector comprises:

obtaining word embeddings for the text of the conversation, wherein a word embedding represents a corresponding word in a third vector space; and

processing the word embeddings with the first neural network.

3 . The computer-implemented method of claim 1 , comprising:

selecting a second designated message using the first context score; and

presenting the second designated message as a suggested message to the user.

4 . The computer-implemented method of claim 1 , comprising:

obtaining text encodings for text of the first designated message; and

computing the first designated message feature vector by processing the text encodings for the text of the first designated message with a response encoder neural network.

5 . The computer-implemented method of claim 1 , comprising:

obtaining input text entered by the user;

computing a first input text score for the first designated message; and

selecting the first designated message using the first input text score.

6 . The computer-implemented method of claim 1 , comprising:

obtaining input text entered by the user;

obtaining text encodings for the input text;

computing an input text feature vector by processing the text encodings of the input text with a response encoder neural network;

computing a first semantic score for the first designated message using the input text feature vector and the first designated message feature vector; and

selecting the first designated message using the first semantic score.

7 . The computer-implemented method of claim 6 , wherein computing the first semantic score comprises computing a distance or a cosine similarity between the input text feature vector and the first designated message feature vector.

8 . The computer-implemented method of claim 1 , wherein the second neural network comprises a multi-layer perceptron.

9 . A system, comprising:

at least one server computer comprising at least one processor and at least one memory, the at least one server computer configured to:

obtain a user embedding vector corresponding to a user, wherein the user embedding vector represents the user in a first vector space;

receive text of a conversation with the user;

compute a conversation feature vector using the text of the conversation and a first neural network, wherein the conversation feature vector represents the conversation in a second vector space;

obtain a set of designated messages, wherein each designated message is associated with a corresponding designated message feature vector;

compute a first context score for a first designated message of the set of designated messages by processing the user embedding vector, the conversation feature vector, and a first designated message feature vector with a second neural network;

select the first designated message using the first context score; and

present the first designated message as a suggested message to the user.

10 . The system of claim 9 , wherein the user embedding vector is trained using messages sent by the user and represents a conversational style or conversational preferences of the user.

11 . The system of claim 9 , wherein the user is a customer support agent of a company and the system is implemented by a third-party company providing services to the company.

12 . The system of claim 9 , wherein the at least one server computer is configured to:

obtain input text entered by the user;

obtain a first prefix score for the first designated message by comparing the input text to a prefix of the first designated message; and

select the first designated message using the first prefix score.

13 . The system of claim 12 , wherein a prefix score has a first value if the input text is not a prefix of a corresponding designated message and has a second value if the input text is a prefix of the corresponding designated message.

14 . The system of claim 9 , wherein the at least one server computer is configured to:

compute a first semantic score for the first designated message;

compute a first prefix score for the first designated message; and

select the first designated message using the first context score and the first prefix score.

15 . The system of claim 9 , wherein the first neural network comprises a recurrent neural network.

16 . The system of claim 9 , wherein the at least one server computer is configured to compute the first context score by processing conversation metadata with the second neural network.

17 . One or more non-transitory, computer-readable media comprising computer-executable instructions that, when executed, cause at least one processor to perform actions comprising:

obtaining a user embedding vector corresponding to a user, wherein the user embedding vector represents the user in a first vector space;

receiving text of a conversation with the user;

computing a conversation feature vector using the text of the conversation and a first neural network, wherein the conversation feature vector represents the conversation in a second vector space;

obtaining a set of designated messages, wherein each designated message is associated with a corresponding designated message feature vector;

computing a first context score for a first designated message of the set of designated messages by processing the user embedding vector, the conversation feature vector, and a first designated message feature vector with a second neural network;

selecting the first designated message using the first context score; and

presenting the first designated message as a suggested message to the user.

18 . The one or more non-transitory, computer-readable media of claim 17 , wherein the conversation is between the user and an automated natural language processing system.

19 . The one or more non-transitory, computer-readable media of claim 17 , wherein the second neural network was trained using negative sampling.

20 . The one or more non-transitory, computer-readable media of claim 17 , wherein the user embedding vector was computed during training of the second neural network.

21 . The one or more non-transitory, computer-readable media of claim 17 , wherein the set of designated messages was obtained from a data store of customer support messages of a company.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 29, 2024
From: BALL, KELSEY TAYLOR; LEI, TAO; FOX, CHRISTOPHER DAVID; HACKMAN, JOSEPH ELLSWORTH
To: ASAPP, INC.
Reel/Frame 066280/0859 →
Continuity (2)
Continuation 16663872 · Oct 25, 2019
Related Publication 20220337538A1 · Oct 20, 2022
References Cited (334)
US 4941178A · Chuang · 1990 [cited by applicant]
US 5195034A · Garneau et al. · 1993 [cited by applicant]
US 5227971A · Nakajima et al. · 1993 [cited by applicant]
US 5369575A · Lamberti et al. · 1994 [cited by applicant]
US 5627940A · Rohra et al. · 1997 [cited by applicant]
US 6173279B1 · Levin et al. · 2001 [cited by applicant]
US 6177932B1 · Galdes et al. · 2001 [cited by applicant]
US 6327363B1 · Henderson et al. · 2001 [cited by applicant]
US 6356869B1 · Chapados et al. · 2002 [cited by applicant]
US 6381645B1 · Sassin et al. · 2002 [cited by applicant]
US 6453292B2 · Ramaswamy et al. · 2002 [cited by applicant]
US 6823313B1 · Yuchimiuk et al. · 2004 [cited by applicant]
US 6915254B1 · Heinze et al. · 2005 [cited by applicant]
US 6981040B1 · Konig et al. · 2005 [cited by applicant]
US H2187H · Yuchimiuk et al. · 2007 [cited by applicant]
US 7257564B2 · Loughmiller et al. · 2007 [cited by applicant]
US 7603330B2 · Gupta et al. · 2009 [cited by applicant]
US 7644057B2 · Nelken et al. · 2010 [cited by applicant]
US 8024196B1 · Wodtke et al. · 2011 [cited by applicant]
US 8054951B1 · Winslow et al. · 2011 [cited by applicant]
US 8156138B2 · Kohn et al. · 2012 [cited by applicant]
US 8271403B2 · Rieck et al. · 2012 [cited by applicant]
US 8494152B1 · Roberts et al. · 2013 [cited by applicant]
US 8577671B1 · Barve et al. · 2013 [cited by applicant]
US 8626509B2 · Roy et al. · 2014 [cited by applicant]
US 8812292B2 · Ceusters et al. · 2014 [cited by applicant]
US 8972240B2 · Brockett et al. · 2015 [cited by applicant]
US 9043197B1 · Pasca et al. · 2015 [cited by applicant]
US 9336269B1 · Smith et al. · 2016 [cited by applicant]
US 9591136B1 · Garcia et al. · 2017 [cited by applicant]
US 9602987B1 · Wittig · 2017 [cited by applicant]
US 9715496B1 · Sapoznik et al. · 2017 [cited by applicant]
US 9723151B2 · McGann et al. · 2017 [cited by applicant]
US 9727925B2 · Subramanian et al. · 2017 [cited by applicant]
US 9742916B1 · Christopher · 2017 [cited by applicant]
US 9761222B1 · Scarasso et al. · 2017 [cited by applicant]
US 9762733B1 · Ramanujaiaha et al. · 2017 [cited by applicant]
US 9805371B1 · Sapoznik et al. · 2017 [cited by applicant]
US 9807037B1 · Sapoznik et al. · 2017 [cited by applicant]
US 9875440B1 · Commons · 2018 [cited by applicant]
US 9892414B1 · Henry · 2018 [cited by applicant]
US 9911413B1 · Kumar et al. · 2018 [cited by applicant]
US 10049663B2 · Orr et al. · 2018 [cited by applicant]
US 10067938B2 · Bellegarda · 2018 [cited by applicant]
US 10083451B2 · Sehrawat et al. · 2018 [cited by applicant]
US 10083690B2 · Giuli et al. · 2018 [cited by applicant]
US 10089072B2 · Piersol et al. · 2018 [cited by applicant]
US 10097690B1 · Henry · 2018 [cited by applicant]
US 10109275B2 · Henry · 2018 [cited by applicant]
US 10127908B1 · Deller et al. · 2018 [cited by applicant]
US 10169315B1 · Heckel et al. · 2019 [cited by applicant]
US 10210244B1 · Branavan et al. · 2019 [cited by applicant]
US 10387888B2 · Sapoznik et al. · 2019 [cited by applicant]
US 10410655B2 · Malur Srinivasan et al. · 2019 [cited by applicant]
US 10453074B2 · Sapoznik et al. · 2019 [cited by applicant]
US 10482875B2 · Henry · 2019 [cited by applicant]
US 10489792B2 · Hackman et al. · 2019 [cited by applicant]
US 10490182B1 · Madhavaraj et al. · 2019 [cited by applicant]
US 10497004B2 · Shaev et al. · 2019 [cited by applicant]
US 10599774B1 · Luo et al. · 2020 [cited by applicant]
US 10650311B2 · Henry · 2020 [cited by applicant]
US 10733614B2 · Sapoznik et al. · 2020 [cited by applicant]
US 10747957B2 · Wohlwend · 2020 [cited by applicant]
US 10878181B2 · Heckel et al. · 2020 [cited by applicant]
US 11216510B2 · Jiang et al. · 2022 [cited by applicant]
US 11425064B2 · Ball · 2022 [cited by examiner]
US 20030033288A1 · Shanahan et al. · 2003 [cited by applicant]
US 20030033347A1 · Bolle et al. · 2003 [cited by applicant]
US 20030103621A1 · Brown et al. · 2003 [cited by applicant]
US 20040003283A1 · Goodman et al. · 2004 [cited by applicant]
US 20040254904A1 · Nelken et al. · 2004 [cited by applicant]
US 20050076084A1 · Loughmiller et al. · 2005 [cited by applicant]
US 20050105712A1 · Williams et al. · 2005 [cited by applicant]
US 20050228790A1 · Ronnewinkel et al. · 2005 [cited by applicant]
US 20060112127A1 · Krause et al. · 2006 [cited by applicant]
US 20060173776A1 · Shalley et al. · 2006 [cited by applicant]
US 20070094217A1 · Ronnewinkel et al. · 2007 [cited by applicant]
US 20070100618A1 · Lee et al. · 2007 [cited by applicant]
US 20070100625A1 · Silvera et al. · 2007 [cited by applicant]
US 20070121899A1 · Galvin · 2007 [cited by applicant]
US 20070156406A1 · Barton et al. · 2007 [cited by applicant]
US 20070168448A1 · Garbow et al. · 2007 [cited by applicant]
US 20080091435A1 · Strope et al. · 2008 [cited by applicant]
US 20080112620A1 · Jiang · 2008 [cited by applicant]
US 20080168070A1 · Naphade et al. · 2008 [cited by applicant]
US 20080168135A1 · Redlich et al. · 2008 [cited by applicant]
US 20090018829A1 · Kuperstein · 2009 [cited by applicant]
US 20090306967A1 · Nicolov et al. · 2009 [cited by applicant]
US 20110066634A1 · Phillips et al. · 2011 [cited by applicant]
US 20110082825A1 · Sathish et al. · 2011 [cited by applicant]
US 20110093414A1 · Xu et al. · 2011 [cited by applicant]
US 20110173346A1 · Neben · 2011 [cited by applicant]
US 20110239113A1 · Hung et al. · 2011 [cited by applicant]
US 20110270771A1 · Coursimault et al. · 2011 [cited by applicant]
US 20110286596A1 · Gressel et al. · 2011 [cited by applicant]
US 20110314012A1 · Kenthapadi et al. · 2011 [cited by applicant]
US 20120005515A1 · Reddi et al. · 2012 [cited by applicant]
US 20120053945A1 · Gupta et al. · 2012 [cited by applicant]
US 20120102130A1 · Guyot et al. · 2012 [cited by applicant]
US 20120239380A1 · Cumby et al. · 2012 [cited by applicant]
US 20120266258A1 · Tuchman et al. · 2012 [cited by applicant]
US 20130027612A1 · Miloseski et al. · 2013 [cited by applicant]
US 20130035931A1 · Ferrucci et al. · 2013 [cited by applicant]
US 20130143587A1 · Williams et al. · 2013 [cited by applicant]
US 20130144605A1 · Brager et al. · 2013 [cited by applicant]
US 20130166485A1 · Hoffmann et al. · 2013 [cited by applicant]
US 20130173604A1 · Li et al. · 2013 [cited by applicant]
US 20130311997A1 · Gruber et al. · 2013 [cited by applicant]
US 20130317808A1 · Kruel et al. · 2013 [cited by applicant]
US 20140040748A1 · Lemay et al. · 2014 [cited by applicant]
US 20140047234A1 · Davis et al. · 2014 [cited by applicant]
US 20140075004A1 · Van Dusen et al. · 2014 [cited by applicant]
US 20140079195A1 · Srivastava et al. · 2014 [cited by applicant]
US 20140180788A1 · George et al. · 2014 [cited by applicant]
US 20140278379A1 · Coccaro et al. · 2014 [cited by applicant]
US 20140282138A1 · Hopton et al. · 2014 [cited by applicant]
US 20140297268A1 · Govrin et al. · 2014 [cited by applicant]
US 20140297281A1 · Togawa et al. · 2014 [cited by applicant]
US 20140314225A1 · Riahi et al. · 2014 [cited by applicant]
US 20140317030A1 · Shen et al. · 2014 [cited by applicant]
US 20140330818A1 · Raina et al. · 2014 [cited by applicant]
US 20140365209A1 · Evermann · 2014 [cited by applicant]
US 20150006143A1 · Skiba et al. · 2015 [cited by applicant]
US 20150032724A1 · Thirugnanasundaram et al. · 2015 [cited by applicant]
US 20150033106A1 · Stetson et al. · 2015 [cited by applicant]
US 20150052002A1 · Welch et al. · 2015 [cited by applicant]
US 20150073798A1 · Karov et al. · 2015 [cited by applicant]
US 20150142704A1 · London · 2015 [cited by applicant]
US 20150149177A1 · Kalns et al. · 2015 [cited by applicant]
US 20150154285A1 · Saarinen et al. · 2015 [cited by applicant]
US 20150178390A1 · Torras · 2015 [cited by applicant]
US 20150195406A1 · Dwyer et al. · 2015 [cited by applicant]
US 20150215624A1 · Wei · 2015 [cited by applicant]
US 20150220833A1 · Le · 2015 [cited by applicant]
US 20150220995A1 · Guyot et al. · 2015 [cited by applicant]
US 20150228275A1 · Watanabe et al. · 2015 [cited by applicant]
US 20150242385A1 · Bao et al. · 2015 [cited by applicant]
US 20150309987A1 · Epstein et al. · 2015 [cited by applicant]
US 20150310377A1 · Duval et al. · 2015 [cited by applicant]
US 20150340032A1 · Gruenstein · 2015 [cited by applicant]
US 20150363393A1 · Williams et al. · 2015 [cited by applicant]
US 20150365387A1 · Good · 2015 [cited by applicant]
US 20160019816A1 · Parry et al. · 2016 [cited by applicant]
US 20160063067A1 · Maitra et al. · 2016 [cited by applicant]
US 20160078456A1 · Chakraborty et al. · 2016 [cited by applicant]
US 20160092688A1 · Wolrich et al. · 2016 [cited by applicant]
US 20160163311A1 · Crook et al. · 2016 [cited by applicant]
US 20160163314A1 · Fujii et al. · 2016 [cited by applicant]
US 20160180151A1 · Philbin et al. · 2016 [cited by applicant]
US 20160180838A1 · Parada San Martin et al. · 2016 [cited by applicant]
US 20160182672A1 · Kuperman et al. · 2016 [cited by applicant]
US 20160239846A1 · Arvapally et al. · 2016 [cited by applicant]
US 20160247068A1 · Lin · 2016 [cited by applicant]
US 20160255034A1 · Yuan · 2016 [cited by applicant]
US 20160323217A1 · Subramani et al. · 2016 [cited by applicant]
US 20160323398A1 · Guo et al. · 2016 [cited by applicant]
US 20160328388A1 · Cao et al. · 2016 [cited by applicant]
US 20160364522A1 · Frey et al. · 2016 [cited by applicant]
US 20170011279A1 · Soldevila et al. · 2017 [cited by applicant]
US 20170013073A1 · Mendez et al. · 2017 [cited by applicant]
US 20170060844A1 · He et al. · 2017 [cited by applicant]
US 20170091320A1 · Psota et al. · 2017 [cited by applicant]
US 20170103324A1 · Weston et al. · 2017 [cited by applicant]
US 20170116982A1 · Gelfenbeyn et al. · 2017 [cited by applicant]
US 20170147682A1 · Alaqeeli et al. · 2017 [cited by applicant]
US 20170148073A1 · Nomula et al. · 2017 [cited by applicant]
US 20170177712A1 · Kopru et al. · 2017 [cited by applicant]
US 20170200077A1 · Weston et al. · 2017 [cited by applicant]
US 20170213138A1 · Bojja et al. · 2017 [cited by applicant]
US 20170286404A1 · Liu et al. · 2017 [cited by applicant]
US 20170308523A1 · Wang et al. · 2017 [cited by applicant]
US 20170323016A1 · Feng et al. · 2017 [cited by applicant]
US 20170336969A1 · Bi · 2017 [cited by applicant]
US 20180005112A1 · Iso-Sipila et al. · 2018 [cited by applicant]
US 20180012231A1 · Sapoznik et al. · 2018 [cited by applicant]
US 20180012232A1 · Sehrawat et al. · 2018 [cited by applicant]
US 20180013699A1 · Sapoznik et al. · 2018 [cited by applicant]
US 20180018562A1 · Jung · 2018 [cited by applicant]
US 20180032755A1 · Odinak · 2018 [cited by applicant]
US 20180052664A1 · Zhang et al. · 2018 [cited by applicant]
US 20180068233A1 · Miranda et al. · 2018 [cited by applicant]
US 20180121415A1 · Perez et al. · 2018 [cited by applicant]
US 20180121738A1 · Womack et al. · 2018 [cited by applicant]
US 20180131645A1 · Magliozzi et al. · 2018 [cited by applicant]
US 20180143965A1 · Willson et al. · 2018 [cited by applicant]
US 20180174020A1 · Wu · 2018 [cited by applicant]
US 20180174037A1 · Henry · 2018 [cited by applicant]
US 20180174579A1 · Henry · 2018 [cited by applicant]
US 20180181807A1 · Yankov · 2018 [cited by applicant]
US 20180196796A1 · Wu · 2018 [cited by applicant]
US 20180203848A1 · Perez et al. · 2018 [cited by applicant]
US 20180232434A1 · Geyik et al. · 2018 [cited by applicant]
US 20180239830A1 · Dialani et al. · 2018 [cited by applicant]
US 20180253734A1 · Henry · 2018 [cited by applicant]
US 20180260680A1 · Finkelstein et al. · 2018 [cited by applicant]
US 20180285599A1 · Praveen et al. · 2018 [cited by applicant]
US 20180329886A1 · Li et al. · 2018 [cited by applicant]
US 20180357439A1 · Osotio · 2018 [cited by applicant]
US 20180365702A1 · Sehrawat et al. · 2018 [cited by applicant]
US 20180376002A1 · Abraham · 2018 [cited by applicant]
US 20190019197A1 · Roberts et al. · 2019 [cited by applicant]
US 20190019503A1 · Henry · 2019 [cited by applicant]
US 20190051302A1 · Gonzalez et al. · 2019 [cited by applicant]
US 20190088270A1 · Malur Srinivasan et al. · 2019 [cited by applicant]
US 20190102378A1 · Piernot et al. · 2019 [cited by applicant]
US 20190122101A1 · Lei · 2019 [cited by applicant]
US 20190163735A1 · Xu et al. · 2019 [cited by applicant]
US 20190180288A1 · Shaev et al. · 2019 [cited by applicant]
US 20190182383A1 · Shaev et al. · 2019 [cited by applicant]
US 20190213601A1 · Hackman et al. · 2019 [cited by applicant]
US 20190286700A1 · Jayaraman et al. · 2019 [cited by applicant]
US 20190311375A1 · Sapoznik et al. · 2019 [cited by applicant]
US 20190332658A1 · Heckel et al. · 2019 [cited by applicant]
US 20190340201A1 · Havens et al. · 2019 [cited by applicant]
US 20190341021A1 · Shang et al. · 2019 [cited by applicant]
US 20200019609A1 · Yu et al. · 2020 [cited by applicant]
US 20200042613A1 · Jiang et al. · 2020 [cited by applicant]
US 20200043015A1 · Shaev et al. · 2020 [cited by applicant]
US 20200081939A1 · Subramaniam · 2020 [cited by applicant]
US 20200151253A1 · Wohlwend et al. · 2020 [cited by applicant]
US 20200151254A1 · Wohlwend · 2020 [cited by applicant]
US 20200327555A1 · Sapoznik et al. · 2020 [cited by applicant]
US 20200380076A1 · Taylor · 2020 [cited by applicant]
US 20210073461A1 · Heckel et al. · 2021 [cited by applicant]
US 20210082412A1 · Kennewick · 2021 [cited by applicant]
US 20210126881A1 · Ball et al. · 2021 [cited by applicant]
US 20210165960A1 · Eisenschlos et al. · 2021 [cited by applicant]
CN 1633817A · 2005 [cited by applicant]
CN 104598445A · 2015 [cited by applicant]
CN 105009556A · 2015 [cited by applicant]
WO 2018009231A1 · 2018 [cited by applicant]
WO 2018009432A1 · 2018 [cited by applicant]
WO 2018160492A1 · 2018 [cited by applicant]
WO 2019113409A1 · 2019 [cited by applicant]
WO 2019113409A8 · 2019 [cited by applicant]
E. Papegnies, V. Labatut, R. Dufour and G. Linares, “Conversational Networks for Automatic Online Moderation,” in IEEE Transactions on Computational Social Systems, vol. 6, No. 1, pp. 38-55, Feb. 2019. (Year: 2019). [cited by examiner]
K. Misra, H. Devarapalli, T. R. Ringenberg and J. T. Rayz, “Authorship Analysis of Online Predatory Conversations using Character Level Convolution Neural Networks,” 2019 IEEE International Conference on Systems, Man an… [cited by examiner]
“scikit-learn: Machine Learning in Python”, scikit-learn 0.17.1 documentation, http://scikit-learn.org/stable/ (accessed on Sep. 20, 2016), 3 pages. [cited by applicant]
“Using algorithms for advanced customer care”, Nokia Strategic white paper, https://resources.ext.nokia.com/asset/200614 (accessed Jul. 26, 2017), 2017, 11 pages. [cited by applicant]
16908347.4, , “European Application Serial No. 16908347.4, Extended European Search Report mailed Nov. 20, 2019”, ASAPP, Inc., 11 pages. [cited by applicant]
18885265.1, , “European Application Serial No. 18885265.1, Extended European Search Report mailed Mar. 29, 2021”, ASAPP, Inc., 11 pages. [cited by applicant]
Al-Rfou, Rami et al., “Conversational Contextual Cues: The Case of Personalization and History for Response Ranking”, CoRR abs/1606.00372, http://arxiv.org/abs/1606.00372, 2016, 10 pages. [cited by applicant]
Anderson, Jr., Walton O. , “Customer relationship management in an e-business environment”, IEMC'01 Proceedings. Change Management and the New Industrial Revolution. IEMC-2001 (Cat. No. 01CH37286),, doi: 10.1109/IEMC.20… [cited by applicant]
Bahdanau, Dzmitry et al., “Neural Machine Translation by Jointly Learning to Align and Translate”, CoRR abs/1409.0473, http://arxiv.org/abs/1409.0473, May 19, 2016, 15 pages. [cited by applicant]
Bang, Jeesoo et al., “Open-domain personalized dialog system using user-interested topics in system responses”, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), doi: 10.1109/ASRU.2015.7404866… [cited by applicant]
Bartl, Alexander et al., “A retrieval-based dialogue system utilizing utterance and context embeddings”, In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017, pp. 1120-1125. [cited by applicant]
Bengio, Yoshua et al., “A Neural Probabilistic Language Model”, Journal of Machine Learning Research, vol. 3 accessed at http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf, 2003 (accessed on Jan. 3, 2017), pp. 1… [cited by applicant]
Berger, Mark J. , “Large Scale Multi-label Text Classification with Semantic Word Vectors”, published 2015 (Year: 2015), Department of Computer Science, Stanford University, Stanford, CA 94305, [email protected], 20… [cited by applicant]
Black, William J. et al., “Facile: Description of the NE System Used for MUC-7”, Proceedings of the 7th Message Understanding Conference, https://www-nlpir.nist.gov/related_projects/muc/proceedings/muc_7_proceedings/fac… [cited by applicant]
Blei, David M. et al., “Latent Dirichlet Allocation”, Journal of Machine Learning Research, vol. 3, 2003, pp. 993-1022. [cited by applicant]
Brown, Peter F. et al., “Class-Based n-gram Models of Natural Language”, Computational Linguistics, vol. 18, No. 4, accessed at http://aclweb.org/anthology/J/J92/J92-4003.pdf, 1992, pp. 467-479 (accessed on Jan. 3, 2017… [cited by applicant]
Cai, Lijuan et al., “Hierarchical Document Categorization with Support Vector Machines”, CIKM'04, Washington, DC, USA, https://info.cs.uab.edu/zhang/Spam-mining-papers/Hierarchical.Data.Classification.with.Support.Vecto… [cited by applicant]
Carrier, Pierre L. et al., “LSTM Networks for Sentiment Analysis”, http://deeplearning.net/tutorial/lstm.html, accessed on Sep. 20, 2016, 5 pages. [cited by applicant]
Chandar, Sarath et al., “Correlational Neural Networks”, CoRR abs/1504.07225, http://arxiv.org/abs/1504.07225, Oct. 12, 2015, 27 pages. [cited by applicant]
Chen, Mia X. et al., “Gmail Smart Compose: Real-Time Assisted Writing”, arXiv:1906.00080v1 [cs.CL], https://arxiv.org/pdf/1906.00080.pdf (accessed on Mar. 25, 2020), May 17, 2019, 9 pages. [cited by applicant]
Chen, Wenlin et al., “Compressing Neural Networks with the Hashing Trick”, arXiv:1504.04788v1, https://arxiv.org/pdf/1504.04788.pdf (accessed on Nov. 26, 2018), Apr. 19, 2015, 10 pages. [cited by applicant]
Chen, Yun-Nung et al., “An empirical investigation of sparse log-linear models for improved dialogue act classification”, IEEE International Conference On Acoustics Speech and Signal Processing, 2013, XP032507868,, 6 pa… [cited by applicant]
Chen, Yun-Nung et al., “An empirical investigation of sparse log-linear models for improved dialogue act classification”, 2013 IEEE International Conference on Acoustics. Speech and Signal Processing. IEEE., 2013, 6 pag… [cited by applicant]
Chen, Yun-Nung , “An empirical investigation of sparse log-linear models for improved dialogue act classification.”, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2013, pp. 1-6. [cited by applicant]
Cheng, Yong et al., “Semi-Supervised Learning for Neural Machine Translation”, CoRR abs/1606.04596, http://arxiv.org/abs/1606.04596, Dec. 10, 2016, 10 pages. [cited by applicant]
Collados, Jose C. , “On the contribution of neural networks and word embeddings in Natural Language Processing”, published at Medium.com, Jan. 2018. (Year: 2018), http://www.josecamachocollados.com, Jan. 2018, pp. 1-7. [cited by applicant]
Courbariaux, Matthieu et al., “BinaryConnect: Training Deep Neural Networks with binary weights during propagations”, CoRR, accessed at https://arxiv.org/pdf/1511.00363v3.pdf, 2015 (Jan. 3, 2017), 9 pages. [cited by applicant]
De La Briandais, Rene , “File Searching Using Variable Length Keys”, Proceedings of the 1959 Western Joint Computer Conference, https://pdfs.semanticscholar.org/3ce3/f4cc1c91d03850ed84ef96a08498e018d18f.pdf, 1959, pp. 2… [cited by applicant]
Deerwester, Scott et al., “Improving Information Retrieval with Latent Semantic Indexing”, Proceedings of the 51st ASIS Annual Meeting (ASIS '88), vol. 25, Oct. 1988, pp. 36-40. [cited by applicant]
Deerwester, Scott et al., “Indexing by Latent Semantic Analysis”, Journal of the Association for Information Science and Technology, vol. 41, iss. 6, Sep. 1990, pp. 391-407. [cited by applicant]
Frome, Andrea et al., “Large-scale Privacy Protection in Google Street View”, 2009 IEEE 12th International Conference on Computer Vision https://pdfs.semanticscholar.org/e645/6ac0e57396f222689dc71a310c2779a31488.pdf, Se… [cited by applicant]
Gong, Yunchao et al., “Iterative Quantization: A Procrustean Approach to Learning Binary Codes”, CVPR 2011, accessed at http://www.cs.unc.edu/˜lazebnik/publications/cvpr11_small_code.pdf, 2011 (accessed on Jan. 3, 2017)… [cited by applicant]
Goodfellow, Ian J et al., “Generative Adversarial Nets”, Advances in Neural Information Processing Systems 27 (NIPS 2014), https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf, 2014, p. 2672-2680. [cited by applicant]
Gray, R. , “Vector quantization”, IEEE ASSP Magazine, vol. 1, iss. 2 https://ieeexplore.ieee.org/document/1162229/ (abstract only), Apr. 1984, pp. 4-29. [cited by applicant]
Henderson, Matthew et al., “Efficient Natural Language Response Suggestion for Smart Reply”, arXiv:1705.00652v1, https://arxiv.org/pdf/1705.00652.pdf (accessed Aug. 15, 2018), May 1, 2017, 15 pages. [cited by applicant]
Hochreiter, Sepp et al., “Long Short-Term Memory”, Neural Computation, vol. 9, iss. 8, 1997, pp. 1735-1780. [cited by applicant]
Hochreitner, Sepp et al., “Long Short-Term Memory”, Neural Computation, vol. 9, Iss. 8, accessed at http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_Istm.pdf, 1997 (accessed on Jan. 3, 2017), pp. 1735-1780. [cited by applicant]
Hu, Baotian et al., “Convolutional Neural Network Architectures for Matching Natural Language Sentences”, CoRR, abs/1503.03244, 2015, 9 pages. [cited by applicant]
Huang, Zhiheng et al., “Bidirectional LSTM-CRF Models for Sequence Tagging”, arXiv:1508.01991v1, https://arxiv.org/pdf/1508.01991.pdf, Aug. 9, 2015, 10 pages. [cited by applicant]
Iyyer, Mohit et al., “Deep Unordered Composition Rivals Syntactic Methods for Text Classification”, Association for Computational Linguistics (ACL), http://cs.umd.edu/˜miyyer/pubs/2015_acl_dan.pdf, 2015, 11 pages. [cited by applicant]
Ji, Zongcheng et al., “An Information Retrieval Approach to Short Text Conversation”, CoRR, accessed at https://arxiv.org/pdf/1408.6988v1.pdf, Aug. 29, 2014 (accessed on Jan. 3, 2017), 21 pages. [cited by applicant]
Kannan, Anjuli et al., “Smart Reply: Automated Response Suggestion for Email”, CoRR, abs/1606.04870, Jun. 15, 2016, 10 pages. [cited by applicant]
Karpathy, Andrej , “The Unreasonable Effectiveness of Recurrent Neural Networks”, http://karpathy.github.io/2015/05/21/rnn-effectiveness/, (accessed on Sep. 20, 2016), May 21, 2015, 42 pages. [cited by applicant]
Kim, Yoon et al., “Character-Aware Neural Language Models”, CoRR abs/1508.06615, http://arxiv.org/abs/1508.06615, 2015, 9 pages. [cited by applicant]
Kim, Yoon , “Convolutional Neural Networks for Sentence Classification”, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Oct. 25-29, 2014, Doha, Qatar, 2014, pp. 1746-1751. [cited by applicant]
Kiros, Ryan et al., “Skip-Thought Vectors”, Proceedings of the 28th International Conference on Neural Information Processing Systems, vol. 2., 2015, pp. 3294-3302. [cited by applicant]
Kuo, Hong-Kwang J. et al., “Advances in natural language call routing”, Bell Labs Technical Journal, vol. 7, No. 4, doi: 10.1002/bltj.10040., 2003, pp. 155-170. [cited by applicant]
Lafferty, John et al., “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data”, https://repository.upenn.edu/cgi/viewcontent.cgi?article=1162&context=cis_papers, The definitive versio… [cited by applicant]
Lai, Hanjiang et al., “Simultaneous Feature Learning and Hash Coding with Deep Neural Networks”, CoRR, accessed at http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Lai_Simultaneous_Feature_Learni ng_2015… [cited by applicant]
Lai, Siwei et al., “Recurrent Convolutional Neural Networks for Text Classification”, AAAI'15 Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015, pp. 2267-2273. [cited by applicant]
Lample, Guillaume et al., “Unsupervised Machine Translation Using Monolingual Corpora Only”, CoRR abs/1711.00043, http://arxiv.org/abs/1711.00043, Oct. 31, 2017, 12 pages. [cited by applicant]
Larochelle, Hugo et al., “A Neural Autoregressive Topic Model”, Advances in Neural Information Processing Systems 25, Editors: F. Pereira and C. J. C. Burges and L. Bottou and K. Q. Weinberger, Published by Curran Assoc… [cited by applicant]
Le, Quoc et al., “Distributed Representations of Sentences and Documents”, Proceedings of the 31st International Conference on Machine Learning, W&CP vol. 32, Beijing, China, 2014, 9 pages. [cited by applicant]
Lee, Cheongjae et al., “Robust Dialog Management with N-best Hypotheses Using Dialog Examples and Agenda”, Proceedings of ACL-08: HLT, Columbus, Ohio, Jun. 2008, pp. 630-637. [cited by applicant]
Levy, Omer et al., “Neural Word Embedding as Implicit Matrix Factorization”, Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, Dec. 8-13, 2014, Montre… [cited by applicant]
Li, Jiwei et al., “A Hierarchical Neural Autoencoder for Paragraphs and Documents”, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on N… [cited by applicant]
Lin, Hui et al., “Multi-document Summarization via Budgeted Maximization of Submodular Functions”, In Proceed. of Human Language Technologies: The Conf. of the North American Chapter of the Association for Computational… [cited by applicant]
Lin, Zhouhan et al., “A Structured Self-Attentive Sentence Embedding”, arXiv:1703.03130v1, https://arxiv.org/pdf/1703.03130.pdf (accessed Jul. 13, 2017), Mar. 9, 2017, 15 pages. [cited by applicant]
Liu, Bingquan et al., “Content-Oriented User Modeling for Personalized Response Ranking in Chatbots”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, No. 1,, Feb. 2018, pp. 122-133. [cited by applicant]
Logeswaran, Lajanugen et al., “An Efficient Framework for Learning Sentence Representations”, International Conference on Learning Representations, https://openreview.net/pdf?id=rJvJXZbOW, 2018, 16 pages. [cited by applicant]
Logeswaran, Lajanugen et al., “An efficient framework for learning sentence representations”, ICLR 2018 Conference Blind Submission, https://openreview.net/pdf?id=rJvJXZb0W (accessed Nov. 26, 2018), Feb. 15, 2018, 16 pa… [cited by applicant]
Miao, Yishu et al., “Neural Variational Inference for Text Processing”, CoRR abs/1511.06038, http://arxiv.org/abs/1511.06038, 2015, 15 pages. [cited by applicant]
Mikolov, Tomas et al., “Distributed Representations of Words and Phrases and their Compositionality”, CoRR abs/1310.4546, http://arxiv.org/abs/1310.4546, 2013, 9 pages. [cited by applicant]
Mikolov, Tomas et al., “Distributed Representations of Words and Phrases and their Compositionality”, CoRR, accessed at https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compos… [cited by applicant]
Mikolov, Tomas et al., “Recurrent neural network based language model”, Interspeech 2010, Sep. 26-30, 2010, Makuhari, Chiba, Japan, 2010, pp. 1045-1048. [cited by applicant]
Mikolov, Tomas et al., “Recurrent neural network based language model”, INTERSPEECH 2010, Makuhari, Chiba, Japan, accessed at http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS10 0722.p… [cited by applicant]
Miller, Alexander et al., “Key-Value Memory Networks for Directly Reading Documents”, 2016, 10 pages. [cited by applicant]
Muhler, Vincent , “Building a Node.js WebSocket Chat App with Socket.io and React”, Tutorial to Socket.io with JavaScript, Feb. 22, 2018, 12 pages. [cited by applicant]
Pagliardini, Matteo et al., “Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features”, CoRR abs/1703.02507, http://arxiv.org/abs/1703.02507, Jul. 10, 2017, 11 pages. [cited by applicant]
PCT/US2016/049896, , “Application Serial No. PCT/US2016/049896, International Search Report and the Written Opinion mailed May 19, 2017”, 14 pages. [cited by applicant]
PCT/US2016/049896, , “International Application Serial No. PCT/US2016/049896, International Preliminary Report on Patentability mailed Jan. 17, 2019”, ASAPP, Inc., 9 pages. [cited by applicant]
PCT/US2017/040205, , “Application Serial No. PCT/US2017/040205, International Search Report and the Written Opinion mailed Sep. 15, 2017”, 11 pages. [cited by applicant]
PCT/US2017/040205, , “International Application Serial No. PCT/US2017/040205, International Preliminary Report on Patentability mailed Jan. 17, 2019”, ASAPP, Inc., 9 pages. [cited by applicant]
PCT/US2018/064404, , “International Application Serial No. PCT/US2018/064404, International Preliminary Report on Patentability mailed Jun. 18, 2020”, ASPP, Inc., 9 pages. [cited by applicant]
PCT/US2018/064404, , “International Application Serial No. PCT/US2018/064404, International Search Report and Written Opinion mailed Feb. 25, 2019”, ASAPP, Inc., 9 pages. [cited by applicant]
PCT/US2019/014569, , “International Application Serial No. PCT/US2019/014569, International Search Report and Written Opinion mailed Mar. 4, 2019”, ASAPP, Inc., 13 pages. [cited by applicant]
Pennington, Jeffrey et al., “GloVe: Global Vectors for Word Representation”, Empirical Methods in Natural Language Processing (EMNLP), http://www.aclweb.org/anthology/D14-1162, (accessed on Nov. 26, 2018 from https://nl… [cited by applicant]
Rush, Alexander M. et al., “Optimal Beam Search for Machine Translation”, Proceedings of EMNLP 2013, Oct. 18-21, 2013, Seattle, Washington, USA, 2013, 12 pages. [cited by applicant]
Scott, Tyler R. et al., “Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning”, arXiv:1805.08402v4 [cs.LG], https://arxiv.org/pdf/1805.08402.pdf, Oct. 27, 2018, 12 pages. [cited by applicant]
See, Abigail et al., “Get To The Point: Summarization with Pointer-Generator Networks”, CoRR abs/1704.04368, http://arxiv.org/abs/1704.04368, Apr. 25, 2017, 20 pages. [cited by applicant]
Shen, Tianxiao et al., “Style Transfer from Non-Parallel Text by Cross-Alignment”, CoRR abs/1705.09655, http://arxiv.org/abs/1705.09655, Nov. 6, 2017, 12 pages. [cited by applicant]
Shi, Yangyang et al., “Contextual spoken language understanding using recurrent neural networks.”, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015, pp. 5271-5275. [cited by applicant]
Sneiders, Eriks , “Automated question answering: template-based approach”, Department of Computer and Systems Sciences, Royal Institute of Technology and Stockholm University (PhD dissertation, Data-och systemvetenskap)… [cited by applicant]
Snell, Jake et al., “Prototypical Networks for Few-shot Learning”, arXiv:1703.05175v2 [cs.LG], https://arxiv.org/pdf/1703.05175.pdf, Jun. 19, 2017, 13 pages. [cited by applicant]
Sutskever, Ilya et al., “Sequence to Sequence Learning with Neural Networks”, CoRR abs/1409.3215, http://arxiv.org/abs/1409.3215, 2014, 9 pages. [cited by applicant]
Tai, Kai S. et al., “Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks”, arXiv:1503.00075v3 [cs.CL], https://arxiv.org/pdf/1503.00075v3.pdf (accessed on Jan. 3, 2017), May 30, 2015, … [cited by applicant]
Ustinova, Evgeniya et al., “Learning Deep Embeddings with Histogram Loss”, arXiv:1611.00822v1 [cs.CV], https://arxiv.org/pdf/1611.00822.pdf, Nov. 2, 2016, 9 pages. [cited by applicant]
Vincent, Pascal et al., “Extracting and Composing Robust Features with Denoising Autoencoders”, Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland, http://www.cs.toronto.edu/˜laroche… [cited by applicant]
Vincent, Pascal et al., “Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion”, Journal of Machine Learning Research 11, http://www.jmlr.org/papers/volume11/… [cited by applicant]
Mnyals, Oriol et al., “A Neural Conversational Model”, CoRR abs/1506.05869, http://arxiv.org/abs/1506.05869, 2015, 8 pages. [cited by applicant]
Wang, Peilu et al., “Word embedding for recurrent neural network based TTS synthesis”, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), doi: 10.1109/ICASSP.2015.7178898, 2015, pp. … [cited by applicant]
Wang, Sida et al., “Baselines and Bigrams: Simple, Good Sentiment and Topic Classification”, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, Jeju, Republic of Korea, https://www.… [cited by applicant]
Weng, Sung-Shun et al., “Using text classification and multiple concepts to answer e-mails”, Expert Systems with Applications, vol. 26, issue 4, May 2004, pp. 529-543. [cited by applicant]
Xiao, Yijun et al., “Efficient Character-level Document Classification by Combining Convolution and Recurrent Layers”, CoRR abs/1602.00367, https://arxiv.org/pdf/1602.00367.pdf, Feb. 1, 2016, 10 pages. [cited by applicant]
Yu, Mo et al., “Diverse Few-Shot Text Classification with Multiple Metrics”, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies… [cited by applicant]
Zhang, Xiang et al., “Character-level Convolutional Networks for Text Classification”, CoRR abs/1509.01626, http://arxiv.org/abs/1509.01626, 2015, 9 pages. [cited by applicant]
Zhao, Han et al., “Self-Adaptive Hierarchical Sentence Model”, CoRR abs/1504.05070, http://arxiv.org/abs/1504.05070, Apr. 27, 2015, 8 pages. [cited by applicant]
Delashmit, et al., “Recent Developments in Multilayer Perceptron Neural Networks”, Proceedings of the the 7th Annual Memphis Area Engineering and Science Conference MAESC, 2005, 15 pages. [cited by applicant]
Dong, et al., “Question Answering over Freebase with Multi-Column Convolutional Neural Networks”, In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Join… [cited by applicant]