Patents by Inventor Vishal Vishnoi

Vishal Vishnoi has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20230185834
    Abstract: Techniques are disclosed herein for synthesizing synthetic training data to facilitate training a natural language to logical form model. In one aspect, training data can be synthesized from original under a framework based on templates and a synchronous context-free grammar. In one aspect, training data can be synthesized under a framework based on a probabilistic context-free grammar and a translator. In one aspect, training data can be synthesized under a framework based on tree-to-string translation. In one aspect, the synthetic training data can be combined with original training data in order to train a machine learning model to translate an utterance to a logical form.
    Type: Application
    Filed: December 13, 2022
    Publication date: June 15, 2023
    Applicant: Oracle International Corporation
    Inventors: Philip Arthur, Vishal Vishnoi, Mark Edward Johnson, Thanh Long Duong, Srinivasa Phani Kumar Gadde, Balakota Srinivas Vinnakota, Cong Duy Vu Hoang, Steve Wai-Chun Siu, Nitika Mathur, Gioacchino Tangari, Aashna Devang Kanuga
  • Publication number: 20230186161
    Abstract: Techniques are disclosed herein for synthesizing synthetic training data to facilitate training a natural language to logical form model. In one aspect, training data can be synthesized from original under a framework based on templates and a synchronous context-free grammar. In one aspect, training data can be synthesized under a framework based on a probabilistic context-free grammar and a translator. In one aspect, training data can be synthesized under a framework based on tree-to-string translation. In one aspect, the synthetic training data can be combined with original training data in order to train a machine learning model to translate an utterance to a logical form.
    Type: Application
    Filed: December 13, 2022
    Publication date: June 15, 2023
    Applicant: Oracle International Corporation
    Inventors: Philip Arthur, Vishal Vishnoi, Mark Edward Johnson, Thanh Long Duong, Srinivasa Phani Kumar Gadde, Balakota Srinivas Vinnakota, Cong Duy Vu Hoang, Steve Wai-Chun Siu, Nitika Mathur, Gioacchino Tangari, Aashna Devang Kanuga
  • Publication number: 20230169955
    Abstract: Techniques for noise data augmentation for training chatbot systems in natural language processing. In one particular aspect, a method is provided that includes receiving a training set of utterances for training an intent classifier to identify one or more intents for one or more utterances; augmenting the training set of utterances with noise text to generate an augmented training set of utterances; and training the intent classifier using the augmented training set of utterances. The augmenting includes: obtaining the noise text from a list of words, a text corpus, a publication, a dictionary, or any combination thereof irrelevant of original text within the utterances of the training set of utterances, and incorporating the noise text within the utterances relative to the original text in the utterances of the training set of utterances at a predefined augmentation ratio to generate augmented utterances.
    Type: Application
    Filed: November 23, 2022
    Publication date: June 1, 2023
    Applicant: Oracle International Corporation
    Inventors: Elias Luqman Jalaluddin, Vishal Vishnoi, Mark Edward Johnson, Thanh Long Duong, Yu-Heng Hong, Balakota Srinivas Vinnakota
  • Patent number: 11657797
    Abstract: Techniques are described for invoking and switching between chatbots of a chatbot system. In some embodiments, the chatbot system is capable of routing an utterance received while a user is already interacting with a first chatbot in the chatbot system. For instance, the chatbot system may identify a second chatbot based on determining that (i) such an utterance is an invalid input to the first chatbot or (ii) that the first chatbot is attempting to route the utterance to a destination associated with the first chatbot. Identifying the second chatbot can involve computing, using a predictive model, separate confidence scores for the first chatbot and the second chatbot, and then determining that a confidence score for the second chatbot satisfies one or more confidence score thresholds. The utterance is then routed to the second chatbot based on the identifying of the second chatbot.
    Type: Grant
    Filed: April 23, 2020
    Date of Patent: May 23, 2023
    Assignee: ORACLE INTERNATIONAL CORPORATION
    Inventors: Vishal Vishnoi, Xin Xu, Srinivasa Phani Kumar Gadde, Fen Wang, Muruganantham Chinnananchi, Manish Parekh, Stephen Andrew McRitchie, Jae Min John, Crystal C. Pan, Gautam Singaraju, Saba Amsalu Teserra
  • Patent number: 11651768
    Abstract: Techniques for stop word data augmentation for training chatbot systems in natural language processing. In one particular aspect, a computer-implemented method includes receiving a training set of utterances for training an intent classifier to identify one or more intents for one or more utterances; augmenting the training set of utterances with stop words to generate an augmented training set of out-of-domain utterances for an unresolved intent category corresponding to an unresolved intent; and training the intent classifier using the training set of utterances and the augmented training set of out-of-domain utterances. The augmenting includes: selecting one or more utterances from the training set of utterances, and for each selected utterance, preserving existing stop words within the utterance and replacing at least one non-stop word within the utterance with a stop word or stop word phrase selected from a list of stop words to generate an out-of-domain utterance.
    Type: Grant
    Filed: September 9, 2020
    Date of Patent: May 16, 2023
    Assignee: ORACLE INTERNATIONAL CORPORATION
    Inventors: Vishal Vishnoi, Mark Edward Johnson, Elias Luqman Jalaluddin, Balakota Srinivas Vinnakota, Thanh Long Duong, Gautam Singaraju
  • Publication number: 20230141853
    Abstract: Techniques disclosed herein relate generally to language detection. In one particular aspect, a method is provided that includes obtaining a sequence of n-grams of a textual unit; using an embedding layer to obtain an ordered plurality of embedding vectors for the sequence of n-grams; using a deep network to obtain an encoded vector that is based on the ordered plurality of embedding vectors; and using a classifier to obtain a language prediction for the textual unit that is based on the encoded vector. The deep network includes an attention mechanism, and using the embedding layer to obtain the ordered plurality of embedding vectors comprises, for each n-gram in the sequence of n-grams: obtaining hash values for the n-gram; based on the hash values, selecting component vectors from among the plurality of component vectors; and obtaining an embedding vector for the n-gram that is based on the component vectors.
    Type: Application
    Filed: November 4, 2022
    Publication date: May 11, 2023
    Applicant: Oracle International Corporation
    Inventors: Thanh Tien Vu, Poorya Zaremoodi, Duy Vu, Mark Edward Johnson, Thanh Long Duong, Xu Zhong, Vladislav Blinov, Cong Duy Vu Hoang, Yu-Heng Hong, Vinamr Goel, Philip Victor Ogren, Srinivasa Phani Kumar Gadde, Vishal Vishnoi
  • Publication number: 20230136965
    Abstract: In some aspects, a computer obtains a trained conditional random field (CRF) model comprising a set of model parameters learned from training data and stored in a transition matrix. Tag sequences, inconsistent with the tag sequence logic, are identified for the tags within the transition matrix. setting, within the transition matrix, a cost associated with transitioning between the pair of tags to be equal to a predefined hyperparameter value that penalizes the transitioning between the inconsistent pair of tags. The CRF model receives a string of text comprising one or more named entities. The CRF model inputs the string of text into the CRF model having the cost associated with the transitioning between the pair of tags set equal to the predefined hyperparameter value. The CRF model classifies the words within the string of text into different classes which might include the one or more named entities.
    Type: Application
    Filed: October 31, 2022
    Publication date: May 4, 2023
    Applicant: Oracle International Corporation
    Inventors: Thanh Tien Vu, Tuyen Quang Pham, Mark Edward Johnson, Thanh Long Duong, Aashna Devang Kanuga, Srinivasa Phani Kumar Gadde, Vishal Vishnoi
  • Publication number: 20230098783
    Abstract: Techniques are disclosed herein for focused training of language models and end-to-end hypertuning of the framework. In one aspect, a method is provided that includes obtaining a machine learning model pre-trained for language modeling, and post-training the machine learning model for various tasks to generate a focused machine learning model. The post-training includes: (i) training the machine learning model on an unlabeled set of training data pertaining to a task that the machine learning model was pre-trained for as part of the language modeling, and the unlabeled set of training data is obtained with respect to a target domain, a target task, or a target language, and (ii) training the machine learning model on a labeled set of training data that pertains to another task that is an auxiliary task related to a downstream task to be performed using the machine learning model or output from the machine learning model.
    Type: Application
    Filed: September 23, 2022
    Publication date: March 30, 2023
    Applicant: Oracle International Corporation
    Inventors: Poorya Zaremoodi, Cong Duy Vu Hoang, Duy Vu, Dai Hoang Tran, Budhaditya Saha, Nagaraj N. Bhat, Thanh Tien Vu, Tuyen Quang Pham, Adam Craig Pocock, Katherine Silverstein, Srinivasa Phani Kumar Gadde, Vishal Vishnoi, Mark Edward Johnson, Thanh Long Duong
  • Patent number: 11599724
    Abstract: Systems, devices, and methods of the present invention relate to text classification. A text classification system accesses an utterance of text. The utterance includes at least one word. The text classification system generates a parse tree for the utterance. The parse tree includes at least one terminal node with a word type. The terminal node represents a word of the utterance. The text classification system applies one or more rules to the text. The text classification system then classifies the utterance as a question or a request for an autonomous agent to perform an action.
    Type: Grant
    Filed: August 13, 2020
    Date of Patent: March 7, 2023
    Assignee: Oracle International Corporation
    Inventors: Boris Galitsky, Vishal Vishnoi, Anfernee Xu
  • Patent number: 11538457
    Abstract: Techniques for noise data augmentation for training chatbot systems in natural language processing. In one particular aspect, a method is provided that includes receiving a training set of utterances for training an intent classifier to identify one or more intents for one or more utterances; augmenting the training set of utterances with noise text to generate an augmented training set of utterances; and training the intent classifier using the augmented training set of utterances. The augmenting includes: obtaining the noise text from a list of words, a text corpus, a publication, a dictionary, or any combination thereof irrelevant of original text within the utterances of the training set of utterances, and incorporating the noise text within the utterances relative to the original text in the utterances of the training set of utterances at a predefined augmentation ratio to generate augmented utterances.
    Type: Grant
    Filed: September 9, 2020
    Date of Patent: December 27, 2022
    Assignee: ORACLE INTERNATIONAL CORPORATION
    Inventors: Elias Luqman Jalaluddin, Vishal Vishnoi, Mark Edward Johnson, Thanh Long Duong, Yu-Heng Hong, Balakota Srinivas Vinnakota
  • Patent number: 11416777
    Abstract: Techniques herein relate to improving quality of classification models for differentiating different user intents by improving the quality of training samples used to train the classification models. Pairs of user intents that are difficult to differentiate by classification models trained using the given training samples are identified based upon distinguishability scores (e.g., F-scores). For each of the identified pairs of intents, pairs of training samples each including a training sample associated with a first intent and a training sample associated with a second intent in the pair of intents are ranked based upon a similarity score between the two training samples in each pair of training samples. A particular pair of training samples with a highest similarity score is selected and provided as output with a suggestion for modifying the particular pair of training samples.
    Type: Grant
    Filed: September 30, 2020
    Date of Patent: August 16, 2022
    Assignee: Oracle International Corporation
    Inventors: Gautam Singaraju, Jiarui Ding, Vishal Vishnoi, Mark Joseph Sugg, Edward E. Wong
  • Publication number: 20220229991
    Abstract: Techniques are disclosed for systems including techniques for multi-feature balancing for natural langue processors. In an embodiment, a method includes receiving a natural language query to be processed by a machine learning model, the machine learning model utilizing a dataset of natural language phrases for processing natural language queries, determining, based on the machine learning model and the natural language query, a feature dropout value, generating, and based on the natural language query, one or more contextual features and one or more expressional features that may be input to the machine learning model, modifying at least one or the one or more contextual features and the one or more expressional features based on the feature dropout value to generate a set of input features for the machine learning model, and processing the set of input features to cause generating an output dataset for corresponding to the natural language query.
    Type: Application
    Filed: January 20, 2022
    Publication date: July 21, 2022
    Applicant: Oracle International Corporation
    Inventors: Thanh Long Duong, Vishal Vishnoi, Mark Edward Johnson, Elias Luqman Jalaluddin, Tuyen Quang Pham, Cong Duy Vu Hoang, Poorya Zaremoodi, Srinivasa Phani Kumar Gadde, Aashna Devang Kanuga, Zikai Li, Yuanxu Wu
  • Publication number: 20220229993
    Abstract: Techniques are provided for using context tags in named-entity recognition (NER) models. In one particular aspect, a method is provided that includes receiving an utterance, generating embeddings for words of the utterance, generating a regular expression and gazetteer feature vector for the utterance, generating a context tag distribution feature vector for the utterance, concatenating or interpolating the embeddings with the regular expression and gazetteer feature vector and the context tag distribution feature vector to generate a set of feature vectors, generating an encoded form of the utterance based on the set of feature vectors, generating log-probabilities based on the encoded form of the utterance, and identifying one or more constraints for the utterance.
    Type: Application
    Filed: January 19, 2022
    Publication date: July 21, 2022
    Applicant: Oracle International Corporation
    Inventors: Duy Vu, Tuyen Quang Pham, Cong Duy Vu Hoang, Srinivasa Phani Kumar Gadde, Thanh Long Duong, Mark Edward Johnson, Vishal Vishnoi
  • Publication number: 20220230462
    Abstract: A training request including an identifier that is indicative of a type of a machine learning (ML) model that is to be trained is received. A plurality of workers are maintained in a training pool, and a plurality of jobs are maintained in a queue of training jobs. Each worker is configured to train a particular type of ML model. Upon the training request being validated, a training job is created for the request and submitted to the queue of training jobs. For each type of ML model, a first metric and a second metric is obtained. A target metric is computed based on the first and the second metrics. The number of workers included in the training pool is modified based on the target metric.
    Type: Application
    Filed: January 7, 2022
    Publication date: July 21, 2022
    Applicant: Oracle International Corporation
    Inventors: Xin Xu, Suman Mallapura Somasundar, Vishal Vishnoi, Xinwei Zhang, Ping L. Lin
  • Publication number: 20220230000
    Abstract: Techniques are disclosed for systems including techniques for multi-factor modelling for training and utilizing chatbot systems for natural language processing. In an embodiment, a method includes receiving a set of utterance data corresponding to a natural language-based query, determining one or more intents for the chatbot corresponds to a possible context for the natural language-based query and associated with a skill for the chatbot, generating one or more intent classification datasets, each intent classification dataset associated with a probability that the natural language query corresponds to an intent of the one or more intents, generating one or more transformed datasets each corresponding to a skill of one or more skills, determining a first skill of the one or more skills based on the one or more transformed datasets and processing, based on the determined first skill, the set of utterance data to resolve the natural language-based query.
    Type: Application
    Filed: January 18, 2022
    Publication date: July 21, 2022
    Applicant: Oracle International Corporation
    Inventors: Elias Luqman Jalaluddin, Vishal Vishnoi, Mark Edward Johnson, Thanh Long Duong, Ying Xu
  • Publication number: 20220171946
    Abstract: Techniques for using enhanced logit values for classifying utterances and messages input to chatbot systems in natural language processing. A method can include a chatbot system receiving an utterance generated by a user interacting with the chatbot system and inputting the utterance into a machine-learning model including a series of network layers. A final network layer of the series of network layers can include a logit function. The machine-learning model can map a first probability for a resolvable class to a first logit value using the logit function. The machine-learning model can map a second probability for a unresolvable class to an enhanced logit value. The method can also include the chatbot system classifying the utterance as the resolvable class or the unresolvable class based on the first logit value and the enhanced logit value.
    Type: Application
    Filed: November 29, 2021
    Publication date: June 2, 2022
    Applicant: Oracle International Corporation
    Inventors: Ying Xu, Poorya Zaremoodi, Thanh Tien Vu, Cong Duy Vu Hoang, Vladislav Blinov, Yu-Heng Hong, Yakupitiyage Don Thanuja Samodhye Dharmasiri, Vishal Vishnoi, Elias Luqman Jalaluddin, Manish Parekh, Thanh Long Duong, Mark Edward Johnson
  • Publication number: 20220171947
    Abstract: Techniques for using logit values for classifying utterances and messages input to chatbot systems in natural language processing. A method can include a chatbot system receiving an utterance generated by a user interacting with the chatbot system. The chatbot system can input the utterance into a machine-learning model including a set of binary classifiers. Each binary classifier of the set of binary classifiers can be associated with a modified logit function. The method can also include the machine-learning model using the modified logit function to generate a set of distance-based logit values for the utterance. The method can also include the machine-learning model applying an enhanced activation function to the set of distance-based logit values to generate a predicted output. The method can also include the chatbot system classifying, based on the predicted output, the utterance as being associated with the particular class.
    Type: Application
    Filed: November 30, 2021
    Publication date: June 2, 2022
    Applicant: Oracle International Corporation
    Inventors: Ying Xu, Poorya Zaremoodi, Thanh Tien Vu, Cong Duy Vu Hoang, Vladislav Blinov, Yu-Heng Hong, Yakupitiyage Don Thanuja Samodhye Dharmasiri, Vishal Vishnoi, Elias Luqman Jalaluddin, Manish Parekh, Thanh Long Duong, Mark Edward Johnson
  • Publication number: 20220172021
    Abstract: Disclosed herein are techniques for addressing an overconfidence problem associated with machine learning models in chatbot systems. For each layer of a plurality of layers of a machine learning model, a distribution of confidence scores is generated for a plurality of predictions with respect to an input utterance. A prediction is determined for each layer of the machine learning model based on the distribution of confidence scores generated for the layer. Based on the predictions, an overall prediction of the machine learning model is determined. A subset of the plurality of layers are iteratively processed to identify a layer whose assigned prediction satisfies a criterion. A confidence score associated with the assigned prediction of the layer of the machine learning model is assigned as an overall confidence score to be associated with the overall prediction of the machine learning model.
    Type: Application
    Filed: November 16, 2021
    Publication date: June 2, 2022
    Applicant: Oracle International Corporation
    Inventors: Cong Duy Vu Hoang, Thanh Tien Vu, Poorya Zaremoodi, Ying Xu, Vladislav Blinov, Yu-Heng Hong, Yakupitiyage Don Thanuja Samodhye Dharmasiri, Vishal Vishnoi, Elias Luqman Jalaluddin, Manish Parekh, Thanh Long Duong, Mark Edward Johnson
  • Publication number: 20220171930
    Abstract: Techniques for keyword data augmentation for training chatbot systems in natural language processing. In one particular aspect, a method is provided that includes receiving a training set of utterances for training a machine-learning model to identify one or more intents for one or more utterances, augmenting the training set of utterances with out-of-domain (OOD) examples. The augmenting includes: identifying keywords within utterances of the training set of utterances, generating a set of OOD examples with the identified keywords, filtering out OOD examples from the set of OOD examples that have a context substantially similar to context of the utterances of the training set of utterances, and incorporating the set of OOD examples without the filtered OOD examples into the training set of utterances to generate an augmented training set of utterances. Thereafter, the machine-learning model is trained using the augmented training set of utterances.
    Type: Application
    Filed: October 28, 2021
    Publication date: June 2, 2022
    Applicant: Oracle International Corporation
    Inventors: Elias Luqman Jalaluddin, Vishal Vishnoi, Thanh Long Duong, Mark Edward Johnson, Poorya Zaremoodi, Gautam Singaraju, Ying Xu, Vladislav Blinov
  • Publication number: 20220171938
    Abstract: Techniques for out-of-domain data augmentation for training chatbot systems in natural language processing. In one particular aspect, a method is provided that includes receiving a training set of utterances for training a machine-learning model to identify one or more intents for one or more utterances, and augmenting the training set of utterances with out-of-domain (OOD) examples. The augmenting includes: generating a data set of OOD examples, filtering out OOD examples from the data set of OOD examples, determining a difficulty value for each OOD example remaining within the filtered data set of the OOD examples, and generating augmented batches of utterances comprising utterances from the training set of utterances and utterances from the filtered data set of the OOD based on the difficulty value for each OOD. Thereafter, the machine-learning model is trained using the augmented batches of utterances in accordance with a curriculum training protocol.
    Type: Application
    Filed: October 28, 2021
    Publication date: June 2, 2022
    Applicant: Oracle International Corporation
    Inventors: Elias Luqman Jalaluddin, Vishal Vishnoi, Thanh Long Duong, Mark Edward Johnson, Poorya Zaremoodi, Gautam Singaraju, Ying Xu, Vladislav Blinov, Yu-Heng Hong