Navneet N Rao, 23San Francisco, CA

2021031, Oct 14, 2021

Apr 8, 2020

16/843872

- Armonk NY, US
Ming Tan - Malden MA, US
Yang Yu - Acton MA, US
Navneet N. Rao - Arlington MA, US
Saloni Potdar - Arlington MA, US
Haoyu Wang - Somerville MA, US

G06F 40/295
G06F 40/284
H04L 12/58

A mechanism is provided to implement suggestion of new entity types with discriminative importance analysis. The mechanism obtains a list of predefined intents from a chatbot designer. The mechanism receives an input sentence having a target intent within the list of predefined intents. The mechanism performs intent-specific importance analysis on the input sentence to generate an importance score for each token in the input sentence. The mechanism ranks the tokens in the input sentence by importance score and outputs a token with a highest importance score as a candidate entity type.

2021028, Sep 16, 2021

Mar 12, 2020

16/816600

- Armonk NY, US
Haoyu Wang - Somerville MA, US
Saloni Potdar - Arlington MA, US
Yang Yu - Acton MA, US
Navneet N. Rao - Arlington MA, US
Haode Qi - Cambridge MA, US

G10L 15/183
G06F 16/9032
G10L 15/06

A mechanism is provided for implementing an intent segmentation mechanism that segments intent boundaries for multi-intent utterances in a conversational agent. For each term of a set of terms in the utterance from a real-time chat session, a set of adversarial utterances is generated for the utterance. An influence of changing each term is determined so as to identify a term importance value. Utilizing the term importance value, one or more of a change in ranking of the intent of the utterance or a change in confidence with regard to the intent of the utterance is identified. An entropy-based segmentation of the utterance into a plurality of candidate partitions is performed. An associated intent and entropy value are then assigned. Based on a segment with minimum entropy, a call associated with the real-time chat session is directed to an operation associated with an intent of the segment with minimum entropy.

2021025, Aug 19, 2021

Feb 13, 2020

16/789804

- Armonk NY, US
Ming Tan - Malden MA, US
Yang Yu - Acton MA, US
Navneet N. Rao - Arlington MA, US
Ladislav Kunc - Cambridge MA, US
Saloni Potdar - Arlington MA, US

G06F 40/295
G06F 16/23
G06N 20/00
G06Q 30/00
G06F 40/30
H04L 12/58

A mechanism is provided to implement an abnormal entity detection mechanism that facilitates detecting abnormal entities in real-time response systems through weak supervision. For each first intent from an entity labeled workspace that matches a second intent in labeled chat logs, when the entity score associated with each first entity or second entity is above a predefined significance level the first entity or the second entity is recorded. For each first intent from the entity labeled workspace that matches the second intent in the labeled chat logs: responsive to the first entity being recorded and the second entity failing to be recorded, that first entity is removed from the training data as being mistakenly included; or, responsive to the second entity being recorded and the first entity failing to be recorded, that second entity is added as a potential business case to the training data.

2021022, Jul 22, 2021

Jan 22, 2020

16/749163

- Armonk NY, US
Saloni Potdar - Arlington MA, US
Ming Tan - Malden MA, US
Navneet R. Rao - Arlington MA, US

G06F 21/62
G06K 9/34
G06F 40/186
G06K 9/48
G06F 40/279

A mechanism is provided to implement a personally identifiable information (PII) detection mechanism that facilitates privacy protection utilizing template embedding learned from text sequences. Input text is processed using natural language processing to identify one or more pieces of personally identifiable information. A character analysis is performed of each character of each piece of personally identifiable information of the one or more pieces of personally identifiable information to identify a character type of character in the piece of personally identifiable information. For each piece of personally identifiable information and based on the associated identified character type, the identified character type is mapped to an associated template character in a set of template characters in a template character data structure. Utilizing the character-to-template mappings for the one or more pieces of personally identifiable information, an output text is generated that projects the template characters by direct character-level mapping.

2021014, May 13, 2021

Nov 11, 2019

16/679464

- Armonk NY, US
Ladislav Kunc - Cambridge MA, US
Saloni Potdar - Arlington MA, US
Haoyu Wang - Somerville MA, US
Navneet N. Rao - Arlington MA, US

G06F 17/27
G06N 20/00

Provided is a method, system, and computer program product for context-dependent spellchecking. The method comprises receiving context data to be used in spell checking. The method further comprises receiving a user input. The method further comprises identifying an out-of-vocabulary (OOV) word in the user input. An initial suggestion pool of candidate words is identified based, at least in part, on the context data. The method then comprises using a noisy channel approach to evaluate a probability that one or more of the candidate words of the initial suggestion pool is an intended word and should be used as a candidate for replacement of the OOV word. The method further comprises selecting one or more candidate words for replacement of the OOV word. The method further comprises outputting the one or more candidates.