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Senthil K Subramaniam

from Naperville, IL
Age ~56
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Also known as:
Fenthil K Subramaniam, Senthilkumar Subramaniam, L Subramaniam, Senthil M
Related to:
Kamala Lakshmipathy, 51
Connected to:
Abishake Subramanian, 40Ananda R Subramaniam, 53Anandaraman Subramaniam, 76Anandi Subramanian, 57Andrew C Subramanian, 56Anupama Subramaniam, 39Arun A Subramani, 54Aruna Subramaniam, 62Arunkumar K Subramanian, 52Ashok K Subramaniam, 48

Senthil Subramaniam Phones & Addresses

  • Naperville, IL
  • 97 Newberry Ct, Aurora, IL 60504 (630) 692-0917
  • 24 Addison St, Bensenville, IL 60106 (630) 860-2704
  • Milpitas, CA
  • Eagan, MN

Resumes

Resumes

Senthil Subramaniam Photo 1

Lead At Deloitte

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Location:
24 south Addison St, Bensenville, IL 60106
Industry:
Information Technology And Services
Work:
Deloitte
Lead at Deloitte
Interests:
Reading
Gardening
Electronics
Languages:
English
Senthil Subramaniam Photo 2

Senthil Subramaniam

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Senthil Subramaniam

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Senthil Subramaniam

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Senthil Subramaniam

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Senthil Subramaniam

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Publications

Us Patents

System For Optimizing Detection Of Intent[S] By Automated Conversational Bot[S] For Providing Human Like Responses

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US Patent:
20200081939, Mar 12, 2020
Filed:
Jan 28, 2019
Appl. No.:
16/258680
Inventors:
- Noida, IN
Senthil Kumar SUBRAMANIAM - Sunnyvale CA, US
International Classification:
G06F 17/21
G06F 17/27
G10L 15/22
G06F 16/9032
G06F 16/901
G06F 16/29
G10L 15/30
G06N 20/00
Abstract:
Disclosed is a system for optimizing detection of an intent, pertaining to a query, by an automated conversational bot for providing human like responses to a user. An analyzer module builds an intent graph storing input dialogues, utterances, and output dialogues associated to an intent. A builder module fed training data, comprising the intent graph stored in the graph database to an automated conversational bot by enabling a bot builder to fill a bot template associated to each intent with a set of parameters indicating distinct utterances of an intent and output dialogues associated to the distinct utterances. A verification module trains the automated conversational bot through reinforcement learning by providing a feedback to the automated conversational bot. In one aspect, the automated conversational bot may be trained by validating an output dialogue against an input dialogue, received from the caller, with an expected response.
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Senthil K Subramaniam from Naperville, IL, age ~56 Get Report