MACHINE LEARNING BASED TENANT-SPECIFIC CHATBOTS FOR PERFORMING ACTIONS IN A MULTI-TENANT SYSTEM
A multi-tenant system performs custom configuration of a tenant-specific chatbot to process and act upon natural language requests. The multi-tenant system configures the tenant-specific chatbots without requiring tenant-specific training. The multi-tenant system providing a user interface for confi...
Saved in:
Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
---|---|
Format | Patent |
Language | English |
Published |
13.05.2021
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | A multi-tenant system performs custom configuration of a tenant-specific chatbot to process and act upon natural language requests. The multi-tenant system configures the tenant-specific chatbots without requiring tenant-specific training. The multi-tenant system providing a user interface for configuring a tenant-specific set of permitted actions. The multi-tenant system determines a set of example phrases for each of the selected permitted actions. The multi-tenant system receives a natural language request from a user and identifies the action that the user wants to perform. The multi-tenant system uses a neural network to compare the natural language request with example phrases to identify an example phrase that matches the natural language request. The multi-tenant system performs the action corresponding to the matching example phrase. |
---|---|
AbstractList | A multi-tenant system performs custom configuration of a tenant-specific chatbot to process and act upon natural language requests. The multi-tenant system configures the tenant-specific chatbots without requiring tenant-specific training. The multi-tenant system providing a user interface for configuring a tenant-specific set of permitted actions. The multi-tenant system determines a set of example phrases for each of the selected permitted actions. The multi-tenant system receives a natural language request from a user and identifies the action that the user wants to perform. The multi-tenant system uses a neural network to compare the natural language request with example phrases to identify an example phrase that matches the natural language request. The multi-tenant system performs the action corresponding to the matching example phrase. |
Author | Kollu, Srikanth Cook, Thomas Archie Xiong, Caiming Zhang, Kevin Abrahamyan, Armen Gulabrani, Chitra Yang, Xinyi Lalani, Roojuta Soumet, Jean-Marc Liu, Qiong Thakar, Rohiniben Brouk, Victor Xie, Tian Khodani, Minal Hetawal, Amit Keskar, Nitish Shirish Schoen, Kevin Motwani, Vishal Vijayakumar, Manju Badarinath, Adarsha Skucha, Karl Ryszard Zou, Zhihao Rodriguez, Juan Manuel Liu, Johnson Harrison, James Douglas Amsili, Rafael McCann, Bryan Machado, Michael |
Author_xml | – fullname: Thakar, Rohiniben – fullname: Kollu, Srikanth – fullname: Xiong, Caiming – fullname: Abrahamyan, Armen – fullname: Yang, Xinyi – fullname: Vijayakumar, Manju – fullname: McCann, Bryan – fullname: Xie, Tian – fullname: Zhang, Kevin – fullname: Keskar, Nitish Shirish – fullname: Zou, Zhihao – fullname: Liu, Qiong – fullname: Gulabrani, Chitra – fullname: Liu, Johnson – fullname: Machado, Michael – fullname: Harrison, James Douglas – fullname: Soumet, Jean-Marc – fullname: Schoen, Kevin – fullname: Brouk, Victor – fullname: Khodani, Minal – fullname: Lalani, Roojuta – fullname: Motwani, Vishal – fullname: Rodriguez, Juan Manuel – fullname: Skucha, Karl Ryszard – fullname: Badarinath, Adarsha – fullname: Hetawal, Amit – fullname: Cook, Thomas Archie – fullname: Amsili, Rafael |
BookMark | eNqNyz0OgkAQQOEttPDvDpNYk7iIxnZYB5mEXQg7FFaEmLUyQIL3jxo9gNVrvrdUs37ow0K1Fk3OjqAgrB27C6To6QxCDp1EviLDGRswOUpaioesrKGi-h370WiES-eBHSDYphCOviv4qxeyazW_d48pbH5dqW1GYvIojEMbprG7hT4828bHu1jrRJ-OB9T7_9QLVF01vQ |
ContentType | Patent |
DBID | EVB |
DatabaseName | esp@cenet |
DatabaseTitleList | |
Database_xml | – sequence: 1 dbid: EVB name: esp@cenet url: http://worldwide.espacenet.com/singleLineSearch?locale=en_EP sourceTypes: Open Access Repository |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Medicine Chemistry Sciences Physics |
ExternalDocumentID | US2021141865A1 |
GroupedDBID | EVB |
ID | FETCH-epo_espacenet_US2021141865A13 |
IEDL.DBID | EVB |
IngestDate | Fri Jul 19 14:21:12 EDT 2024 |
IsOpenAccess | true |
IsPeerReviewed | false |
IsScholarly | false |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-epo_espacenet_US2021141865A13 |
Notes | Application Number: US201916680323 |
OpenAccessLink | https://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20210513&DB=EPODOC&CC=US&NR=2021141865A1 |
ParticipantIDs | epo_espacenet_US2021141865A1 |
PublicationCentury | 2000 |
PublicationDate | 20210513 |
PublicationDateYYYYMMDD | 2021-05-13 |
PublicationDate_xml | – month: 05 year: 2021 text: 20210513 day: 13 |
PublicationDecade | 2020 |
PublicationYear | 2021 |
RelatedCompanies | salesforce.com, inc |
RelatedCompanies_xml | – name: salesforce.com, inc |
Score | 3.337453 |
Snippet | A multi-tenant system performs custom configuration of a tenant-specific chatbot to process and act upon natural language requests. The multi-tenant system... |
SourceID | epo |
SourceType | Open Access Repository |
SubjectTerms | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC COMMUNICATION TECHNIQUE ELECTRICITY PHYSICS TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION |
Title | MACHINE LEARNING BASED TENANT-SPECIFIC CHATBOTS FOR PERFORMING ACTIONS IN A MULTI-TENANT SYSTEM |
URI | https://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20210513&DB=EPODOC&locale=&CC=US&NR=2021141865A1 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1bS8MwFD4M7286FS9TAkrfinZbN30Y0qapraxZWVKZT6OXCIJsw1X8-550m-5pTyFXTgInJ19yzheAW82nUnTvbbNA-2G20YaYaW4VZrNACJSqLH1UOho54p0gab-M7FENPlexMBVP6E9FjogalaO-l9V-Pfu_xPIq38r5XfaBRdMnX_Y8Y4mOEb_YVsvw3B6LB96AGpT2EmHwYVVnoSgd20GstK0P0pppn726Oi5ltm5U_EPYiXG8SXkENTWpwz5d_b1Wh71o-eRdh93KRzOfY-FSD-fHMI4cGoSckT5zhjzkz8R1BPOIZNzh0hQxo6EfUkIDR7oDKQhiPRKzISaRbr1wHREk5MQhUdKXobnoSsSbkCw6gRufSRqYKPP4b4nGiVifYOsUtibTiToDkj90sve8UJmtum3VLLI8tey8q2xlaabA9Bwam0a62Fx9CQc6q1_TrVYDtsqvb3WFRrrMrqu1_QXj8o2D |
link.rule.ids | 230,309,786,891,25594,76903 |
linkProvider | European Patent Office |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3dS8MwED_G_JhvOhU_pgaUvhXttq76MKRNU1tts7KkMp9KPyII0g1X8d837Tbd054Cd8lxCVyOX-4jADdVP5XcuNPVXPoPtS99iJpkWq52cwmBEpEmD6KqRg7owI36zxN90oDPVS1M3Sf0p26OKC0qk_Ze1vf17P8Ry65zK-e36YckTR8dPrSVJTqW-EXXeoptDUk4skdYwXgYMYWOa54mVRnopsRKW4YEhTVYerWqupTZulNx9mE7lPKK8gAaomhDC6_-XmvDbrAMebdhp87RzOaSuLTD-SHEgYldjxLkE3NMPfqELJMRG3FCTcpVFhLsOR5G2DW5NeIMSayHQjKWQ1DNXqSOMORRZKIg8rmnLpYi9sY4CY7g2iEcu6rUOf47ojhi6xvsHUOzmBbiBFB2P0jfs1ykujD6opunWaLpmSF0oVWdApNT6GySdLaZfQUtlwd-7Hv05Rz2KlYVWdd6HWiWX9_iQjrsMr2sz_kXp3OQbQ |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Apatent&rft.title=MACHINE+LEARNING+BASED+TENANT-SPECIFIC+CHATBOTS+FOR+PERFORMING+ACTIONS+IN+A+MULTI-TENANT+SYSTEM&rft.inventor=Thakar%2C+Rohiniben&rft.inventor=Kollu%2C+Srikanth&rft.inventor=Xiong%2C+Caiming&rft.inventor=Abrahamyan%2C+Armen&rft.inventor=Yang%2C+Xinyi&rft.inventor=Vijayakumar%2C+Manju&rft.inventor=McCann%2C+Bryan&rft.inventor=Xie%2C+Tian&rft.inventor=Zhang%2C+Kevin&rft.inventor=Keskar%2C+Nitish+Shirish&rft.inventor=Zou%2C+Zhihao&rft.inventor=Liu%2C+Qiong&rft.inventor=Gulabrani%2C+Chitra&rft.inventor=Liu%2C+Johnson&rft.inventor=Machado%2C+Michael&rft.inventor=Harrison%2C+James+Douglas&rft.inventor=Soumet%2C+Jean-Marc&rft.inventor=Schoen%2C+Kevin&rft.inventor=Brouk%2C+Victor&rft.inventor=Khodani%2C+Minal&rft.inventor=Lalani%2C+Roojuta&rft.inventor=Motwani%2C+Vishal&rft.inventor=Rodriguez%2C+Juan+Manuel&rft.inventor=Skucha%2C+Karl+Ryszard&rft.inventor=Badarinath%2C+Adarsha&rft.inventor=Hetawal%2C+Amit&rft.inventor=Cook%2C+Thomas+Archie&rft.inventor=Amsili%2C+Rafael&rft.date=2021-05-13&rft.externalDBID=A1&rft.externalDocID=US2021141865A1 |