Srinivas S Sunkara, 48Saratoga, CA

2022030, Sep 22, 2022

Mar 1, 2022

17/684401

- Charlotte NC, US
Srinivas S. Sunkara - Sunnyvale CA, US
Suzzanne D. Usiskin - Palo Alto CA, US

G06Q 20/40
G06Q 20/36
G06Q 20/12
G06Q 20/38

A system and method for creating and accessing a bifurcated digital wallet is described. The method comprises of processor implemented steps of authenticating by an authentication server one or more authentication information sent by a user terminal to the authentication server; extracting a first set of information by the authentication server based on the authentication of the authentication information; extracting a second set of information by one or more second servers based on the authentication of the authentication information; collating the first set of information and second set of information and displaying the collated information at the user terminal for processing a transaction.

2022023, Jul 28, 2022

Jan 19, 2022

17/579052

- Santa Clara CA, US
Edwin Sapugay - Foster City CA, US
Srinivas SatyaSai Sunkara - Saratoga CA, US

G10L 15/18
G10L 15/16
G10L 15/30
G10L 15/06

A natural language understanding (NLU) framework includes a domain-aware vector encoding (DAVE) framework. The DAVE framework enables a designer to create a DAVE system having a domain-agnostic semantic (DAS) model and a corresponding trained vector translator (VT) model. The DAVE system uses the DAS model to generate domain-agnostic semantic vectors for portions of a user utterance, and then uses the VT model to translate the domain-agnostic semantic vectors into a domain-aware semantic vectors to be used by a NLU system of the NLU framework during a meaning search operation. The VT model is also designed to provide predicted intent classifications for the portions the user utterance. Both the NLU system and the DAVE system of the NLU framework are highly configurable and refer to various NLU constraints during operation, including performance constraints and resource constraints provided by a designer or user of the NLU framework.

2022022, Jul 21, 2022

Jan 19, 2022

17/579044

- Santa Clara CA, US
Edwin Sapugay - Foster City CA, US
Sathwik Tejaswi Madhusudhan - Santa Clara CA, US
Anil Kumar Madamala - Sunnyvale CA, US
Hari Subramani - Champaign IL, US
Jonggun Park - Santa Clara CA, US
Srinivas SatyaSai Sunkara - Saratoga CA, US

G06F 40/30
G06F 40/40
H04L 51/10

A natural language understanding (NLU) framework includes a modeling and optimization system that enables enhanced understanding and explainability to the operation of the NLU framework. The NLU framework includes a configuration vector storing settings of various components that may be applied during NLU inference of an utterance, such as which components should be activated or deactivated, as well as which numerical values (e.g., threshold values, coefficients, weight values) that are used by these components during operation. By using this configuration vector to systematically disable and adjust numerical parameters of the components of the NLU framework, and then determining the performance of the NLU framework in these configurations, the modeling and optimization system determines relationships between, as well as the relative importance of, the components of the NLU framework. The modeling and optimization system automatically determines or optimizes configurations for the NLU framework to accommodate various NLU performance and/or resource constraints.

2021034, Nov 4, 2021

Jun 28, 2021

17/304918

- Santa Clara CA, US
Anil Kumar Madamala - Sunnyvale CA, US
Maxim Naboka - Santa Clara CA, US
Srinivas SatyaSai Sunkara - Sunnyvale CA, US
Lewis Savio Landry Santos - Santa Clara CA, US
Murali B. Subbarao - Saratoga CA, US

G06F 40/30
G06F 40/35
G06F 40/295

An agent automation system includes a memory configured to store a reasoning agent/behavior engine (RA/BE) including a first persona and a current context and a processor configured to execute instructions of the RA/BE to cause the first persona to perform actions comprising: receiving intents/entities of a first user utterance; recognizing a context overlay cue in the intents/entities of the first user utterance, wherein the context overlay cue defines a time period; updating the current context of the RA/BE by overlaying context information from at least one stored episode associated with the time period; and performing at least one action based on the intents/entities of the first user utterance and the current context of the RA/BE.

2021029, Sep 23, 2021

Mar 22, 2021

17/208747

- Charlotte NC, US
Srinivas Satyasai SUNKARA - Sunnyvale CA, US

Paymentus Corporation - Charlotte NC

G06Q 30/06
G06Q 30/02
G06Q 50/00
G06F 16/248
G06F 16/2457

Described are systems and methods for product recommendation refinement in a topic-based virtual storefront embedded in a topical community web page. A system and method may facilitate determining user and community member activity in the virtual storefront based on which weighted keywords are derived. A topic set containing various weighted keywords may be iteratively configured for extracting and ordering one or more products that are extracted from a plurality of heterogeneous sources. A server may be configured for refining the topic set by modifying a baseline session keyword weight or a baseline contextual keyword weight based on a total topic set weight, an elasticity parameter, core topic keyword weights associated with core topic keywords, session keyword weights, and/or contextual keyword weights; and identifying, based at least on the configured topic set, products to be presented to a user as product recommendations in the virtual storefront.

2021022, Jul 22, 2021

Mar 24, 2021

17/301092

- Santa Clara CA, US
Anil Kumar Madamala - Sunnyvale CA, US
Maxim Naboka - Santa Clara CA, US
Srinivas SatyaSai Sunkara - Sunnyvale CA, US
Lewis Savio Landry Santos - Santa Clara CA, US
Murali B. Subbarao - Saratoga CA, US

G06F 40/30
G06N 20/00
G10L 15/19
G10L 15/22
G06N 5/02
G06F 40/205
G06F 40/211

An agent automation system includes a memory configured to store a natural language understanding (NLU) framework and a model, wherein the model includes at least one original meaning representation. The system includes a processor configured to execute instructions of the NLU framework to cause the agent automation system to perform actions including: performing rule-based generalization of the model to generate at least one generalized meaning representation of the model from the at least one original meaning representation of the model; performing rule-based refinement of the model to prune or modify the at least one generalized meaning representation of the model, or the at least one original meaning representation of the model, or a combination thereof; and after performing the rule-based generalization and the rule-based refinement of the model, using the model to extract intents/entities from a received user utterance

2020034, Nov 5, 2020

Jul 16, 2020

16/931007

- Santa Clara CA, US
Anil Kumar Madamala - Sunnyvale CA, US
Maxim Naboka - Santa Clara CA, US
Srinivas SatyaSai Sunkara - Sunnyvale CA, US
Lewis Savio Landry Santos - Santa Clara CA, US
Murali B. Subbarao - Saratoga CA, US

G06F 40/30
G06F 16/28
G06F 16/2458
G06N 5/04
G06F 40/247
G06F 40/295

An agent automation system includes a memory configured to store a corpus of utterances and a semantic mining framework and a processor configured to execute instructions of the semantic mining framework to cause the agent automation system to perform actions, wherein the actions include: detecting intents within the corpus of utterances; producing intent vectors for the intents within the corpus; calculating distances between the intent vectors; generating meaning clusters of intent vectors based on the distances; detecting stable ranges of cluster radius values for the meaning clusters; and generating an intent/entity model from the meaning clusters and the stable ranges of cluster radius values, wherein the agent automation system is configured to use the intent/entity model to classify intents in received natural language requests.

2020032, Oct 15, 2020

Jun 23, 2020

16/909731

- Santa Clara CA, US
Anil Kumar Madamala - Sunnyvale CA, US
Maxim Naboka - Santa Clara CA, US
Srinivas SatyaSai Sunkara - Sunnyvale CA, US
Lewis Savio Landry Santos - Santa Clara CA, US
Murali B. Subbarao - Saratoga CA, US

G06F 40/30
G06N 20/00
G10L 15/19
G10L 15/22
G06N 5/02
G06F 40/205
G06F 40/211

An agent automation system includes a memory configured to store a natural language understanding (NLU) framework and a processor configured to execute instructions of the NLU framework to cause the agent automation system to perform actions. These actions comprise: generating an annotated utterance tree of an utterance using a combination of rules-based and machine-learning (ML)-based components, wherein a structure of the annotated utterance tree represents a syntactic structure of the utterance, and wherein nodes of the annotated utterance tree include word vectors that represent semantic meanings of words of the utterance; and using the annotated utterance tree as a basis for intent/entity extraction of the utterance.