Trung Quoc Bui

20220383031, Dec 1, 2022

May 28, 2021

17/333583

- San Jose CA, US
Quan Hung Tran - San Jose CA, US
Kushal Kafle - Boston MA, US
Trung Huu Bui - San Jose CA, US
Franck Dernoncourt - San Jose CA, US
Walter Chang - San Jose CA, US

G06K 9/46
G06K 9/32
G06F 16/51
G06F 16/583
G06F 16/532
G06F 16/56

The present disclosure describes a model for large scale color prediction of objects identified in images. Embodiments of the present disclosure include an object detection network, an attention network, and a color classification network. The object detection network generates object features for an object in an image and may include a convolutional neural network (CNN), region proposal network, or a ResNet. The attention network generates an attention vector for the object based on the object features, wherein the attention network takes a query vector based on the object features, and a plurality of key vector and a plurality of value vectors corresponding to a plurality of colors as input. The color classification network generates a color attribute vector based on the attention vector, wherein the color attribute vector indicates a probability of the object including each of the plurality of colors.

20220383150, Dec 1, 2022

May 26, 2021

17/331131

- San Jose CA, US
Tuan Manh Lai - San Jose CA, US
Trung Bui - San Jose CA, US
Doo Soon Kim - San Jose CA, US

G06N 5/04
G06N 20/00

This disclosure describes methods, non-transitory computer readable storage media, and systems that provide a platform for on-demand selection of machine-learning models and on-demand learning of parameters for the selected machine-learning models via cloud-based systems. For instance, the disclosed system receives a request indicating a selection of a machine-learning model to perform a machine-learning task (e.g., a natural language task) utilizing a specific dataset (e.g., a user-defined dataset). The disclosed system utilizes a scheduler to monitor available computing devices on cloud-based storage systems for instantiating the selected machine-learning model. Using the indicated dataset at a determined cloud-based computing device, the disclosed system automatically trains the machine-learning model. In additional embodiments, the disclosed system generates a dataset visualization, such as an interactive confusion matrix, for interactively viewing and selecting data generated by the machine-learning model.

20220374426, Nov 24, 2022

May 11, 2021

17/317052

- SAN JOSE CA, US
Doo Soon Kim - San Jose CA, US
Franck Dernoncourt - San Jose CA, US
Trung Bui - San Jose CA, US

G06F 16/2452
G06F 16/22
G06N 3/08
G06N 3/04
G06F 40/30

Systems and methods for natural language processing are described. One or more embodiments of the present disclosure receive a query related to information in a table, compute an operation selector by combining the query with an operation embedding representing a plurality of table operations, compute a column selector by combining the query with a weighted operation embedding, compute a row selector based on the operation selector and the column selector, compute a probability value for a cell in the table based on the row selector and the column selector, where the probability value represents a probability that the cell provides an answer to the query, and transmit contents of the cell based on the probability value.

20210375277, Dec 2, 2021

Jun 1, 2020

16/889669

- San Jose CA, US
Trung BUI - San Jose CA, US
Quan Hung TRAN - San Jose CA, US

G10L 15/22
G10L 15/02

A computer-implemented method is disclosed for determining one or more characteristics of a dialog between a computer system and user. The method may comprise receiving a system utterance comprising one or more tokens defining one or more words generated by the computer system; receiving a user utterance comprising one or more tokens defining one or more words uttered by a user in response to the system utterance, the system utterance and the user utterance forming a dialog context; receiving one or more utterance candidates comprising one or more tokens; for each utterance candidate, generating an input sequence combining the one or more tokens of each of the system utterance, the user utterance, and the utterance candidate; and for each utterance candidate, evaluating the generated input sequence with a model to determine a probability that the utterance candidate is relevant to the dialog context.

20210279414, Sep 9, 2021

Mar 5, 2020

16/810345

- SAN JOSE CA, US
WALlTER CHANG - SAN JOSE CA, US
TRUNG BUI - SANJOSE CA, US
QUAN TRAN - SAN JOSE CA, US
FRANCK DERNONCOURT - SUNNYVALE CA, US

G06F 40/211
G06N 3/04
G06N 3/08

Systems and methods for parsing natural language sentences using an artificial neural network (ANN) are described. Embodiments of the described systems and methods may generate a plurality of word representation matrices for an input sentence, wherein each of the word representation matrices is based on an input matrix of word vectors, a query vector, a matrix of key vectors, and a matrix of value vectors, and wherein a number of the word representation matrices is based on a number of syntactic categories, compress each of the plurality of word representation matrices to produce a plurality of compressed word representation matrices, concatenate the plurality of compressed word representation matrices to produce an output matrix of word vectors, and identify at least one word from the input sentence corresponding to a syntactic category based on the output matrix of word vectors.

20210081503, Mar 18, 2021

Sep 12, 2019

16/569513

- San Jose CA, US
Tuan Manh Lai - San Jose CA, US
Trung Bui - San Jose CA, US

G06F 17/28
G06N 5/04
G06F 16/9032
G06F 17/16
G06K 9/62

The present disclosure relates to systems, methods, and non-transitory computer-readable media that can determine an answer to a query based on matching probabilities for combinations of respective candidate answers. For example, the disclosed systems can utilize a gated-self attention mechanism (GSAM) to interpret inputs that include contextual information, a query, and candidate answers. The disclosed systems can also utilize a memory network in tandem with the GSAM to form a gated self-attention memory network (GSAMN) to refine outputs or predictions over multiple reasoning hops. Further, the disclosed systems can utilize transfer learning of the GSAM/GSAMN from an initial training dataset to a target training dataset.

20210058345, Feb 25, 2021

Aug 22, 2019

16/548140

- San Jose CA, US
Franck Dernoncourt - Sunnyvale CA, US
Doo Soon Kim - San Jose CA, US
Trung Bui - San Jose CA, US

H04L 12/58
G06N 3/08
G06F 16/901
G06F 16/903

The present disclosure relates to utilizing a graph neural network to accurately and flexibly identify text phrases that are relevant for responding to a query. For example, the disclosed systems can generate a graph topology having a plurality of nodes that correspond to a plurality of text phrases and a query. The disclosed systems can then utilize a graph neural network to analyze the graph topology, iteratively propagating and updating node representations corresponding to the plurality of nodes, in order to identify text phrases that can be used to respond to the query. In some embodiments, the disclosed systems can then generate a digital response to the query based on the identified text phrases.

20210050014, Feb 18, 2021

Nov 2, 2020

17/086805

- San Jose CA, US
Trung Bui - San Jose CA, US
Hung Bui - Sunnyvale CA, US

G10L 15/22
G10L 15/16
G06N 3/10
G06N 3/04
G10L 15/30

The present disclosure relates to systems, methods, and non-transitory computer readable media for generating dialogue responses based on received utterances utilizing an independent gate context-dependent additive recurrent neural network. For example, the disclosed systems can utilize a neural network model to generate a dialogue history vector based on received utterances and can use the dialogue history vector to generate a dialogue response. The independent gate context-dependent additive recurrent neural network can remove local context to reduce computation complexity and allow for gates at all time steps to be computed in parallel. The independent gate context-dependent additive recurrent neural network maintains the sequential nature of a recurrent neural network using the hidden vector output.