Patents by Inventor Balakota Srinivas Vinnakota

Balakota Srinivas Vinnakota 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).

  • Patent number: 12014146
    Abstract: The present disclosure relates to techniques for identifying out-of-domain utterances.
    Type: Grant
    Filed: August 2, 2023
    Date of Patent: June 18, 2024
    Assignee: Oracle International Corporation
    Inventors: Thanh Long Duong, Mark Edward Johnson, Vishal Vishnoi, Crystal C. Pan, Vladislav Blinov, Cong Duy Vu Hoang, Elias Luqman Jalaluddin, Duy Vu, Balakota Srinivas Vinnakota
  • Publication number: 20240169155
    Abstract: Techniques for automatically switching between chatbot skills in the same domain. In one particular aspect, a method is provided that includes receiving an utterance from a user within a chatbot session, where a current skill context is a first skill and a current group context is a first group, inputting the utterance into a candidate skills model for the first group, obtaining, using the candidate skills model, a ranking of skills within the first group, determining, based on the ranking of skills, a second skill is a highest ranked skill, changing the current skill context of the chatbot session to the second skill, inputting the utterance into a candidate flows model for the second skill, obtaining, using the candidate flows model, a ranking of intents within the second skill that match the utterance, and determining, based on the ranking of intents, an intent that is a highest ranked intent.
    Type: Application
    Filed: January 26, 2024
    Publication date: May 23, 2024
    Applicant: Oracle International Corporation
    Inventors: Vishal Vishnoi, Xin Xu, Elias Luqman Jalaluddin, Srinivasa Phani Kumar Gadde, Crystal C. Pan, Mark Edward Johnson, Thanh Long Duong, Balakota Srinivas Vinnakota, Manish Parekh
  • Patent number: 11972755
    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: November 23, 2022
    Date of Patent: April 30, 2024
    Assignee: Oracle International Corporation
    Inventors: Elias Luqman Jalaluddin, Vishal Vishnoi, Mark Edward Johnson, Thanh Long Duong, Yu-Heng Hong, Balakota Srinivas Vinnakota
  • Publication number: 20240126795
    Abstract: Techniques are disclosed herein for integrating document question answering in an artificial intelligence-based platform, such as a chatbot system. The techniques include receiving a query from a user, rewriting the query to include one or more specific descriptors, computing an embedding vector for the rewritten query, retrieving one or more textual passages from a document store utilizing the embedding vector for the rewritten query, determining one or more answers to the rewritten query within the one or more textual passages, and returning the one or more answers.
    Type: Application
    Filed: October 13, 2023
    Publication date: April 18, 2024
    Applicant: Oracle International Corporation
    Inventors: Xu Zhong, Thanh Long Duong, Mark Edward Johnson, Charles Woodrow Dickstein, King-Hwa Lee, Xin Xu, Srinivasa Phani Kumar Gadde, Vishal Vishnoi, Christopher Kennewick, Balakota Srinivas Vinnakota, Raefer Christopher Gabriel
  • Patent number: 11922123
    Abstract: Techniques for automatically switching between chatbot skills in the same domain. In one particular aspect, a method is provided that includes receiving an utterance from a user within a chatbot session, where a current skill context is a first skill and a current group context is a first group, inputting the utterance into a candidate skills model for the first group, obtaining, using the candidate skills model, a ranking of skills within the first group, determining, based on the ranking of skills, a second skill is a highest ranked skill, changing the current skill context of the chatbot session to the second skill, inputting the utterance into a candidate flows model for the second skill, obtaining, using the candidate flows model, a ranking of intents within the second skill that match the utterance, and determining, based on the ranking of intents, an intent that is a highest ranked intent.
    Type: Grant
    Filed: September 30, 2021
    Date of Patent: March 5, 2024
    Assignee: ORACLE INTERNATIONAL CORPORATION
    Inventors: Vishal Vishnoi, Xin Xu, Elias Luqman Jalaluddin, Srinivasa Phani Kumar Gadde, Crystal C. Pan, Mark Edward Johnson, Thanh Long Duong, Balakota Srinivas Vinnakota, Manish Parekh
  • Publication number: 20230376696
    Abstract: The present disclosure relates to techniques for identifying out-of-domain utterances.
    Type: Application
    Filed: August 2, 2023
    Publication date: November 23, 2023
    Applicant: Oracle International Corporation
    Inventors: Thanh Long Duong, Mark Edward Johnson, Vishal Vishnoi, Crystal C. Pan, Vladislav Blinov, Cong Duy Vu Hoang, Elias Luqman Jalaluddin, Duy Vu, Balakota Srinivas Vinnakota
  • Patent number: 11763092
    Abstract: The present disclosure relates to techniques for identifying out-of-domain utterances.
    Type: Grant
    Filed: March 30, 2021
    Date of Patent: September 19, 2023
    Assignee: Oracle International Corporation
    Inventors: Thanh Long Duong, Mark Edward Johnson, Vishal Vishnoi, Crystal C. Pan, Vladislav Blinov, Cong Duy Vu Hoang, Elias Luqman Jalaluddin, Duy Vu, Balakota Srinivas Vinnakota
  • Publication number: 20230186025
    Abstract: Techniques for preprocessing data assets to be used in a natural language to logical form model based on scalable search and content-based schema linking. In one particular aspect, a method includes accessing an utterance, classifying named entities within the utterance into predefined classes, searching value lists within the database schema using tokens from the utterance to identify and output value matches including: (i) any value within the value lists that matches a token from the utterance and (ii) any attribute associated with a matching value, generating a data structure by organizing and storing: (i) each of the named entities and an assigned class for each of the named entities, (ii) each of the value matches and the token matching each of the value matches, and (iii) the utterance, in a predefined format for the data structure, and outputting the data structure.
    Type: Application
    Filed: December 13, 2022
    Publication date: June 15, 2023
    Applicant: Oracle International Corporation
    Inventors: Jae Min John, Vishal Vishnoi, Mark Edward Johnson, Thanh Long Duong, Srinivasa Phani Kumar Gadde, Balakota Srinivas Vinnakota, Shivashankar Subramanian, Cong Duy Vu Hoang, Yakupitiyage Don Thanuja Samodhye Dharmasiri, Nitika Mathur, Aashna Devang Kanuga, Philip Arthur, Gioacchino Tangari, Steve Wai-Chun Siu
  • 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: 20230186026
    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: 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: 20230185799
    Abstract: Techniques are disclosed for training a model, using multi-task learning, to transform natural language to a logical form. In one particular aspect, a method includes accessing a first set of utterances that have non-follow-up utterances and a second set of utterances that have initial utterances and associated one or more follow-up utterances and training a model for translating an utterance to a logical form. The training is a joint training process that includes calculating a first loss for a first semantic parsing task based on one or more non-follow-up utterances from the first set of utterances, calculating a second loss for a second semantic parsing task based on one or more initial utterances and associated one or more follow-up utterances from the second set of utterances, combining the first and second losses to obtain a final loss, and updating model parameters of the model based on the final loss.
    Type: Application
    Filed: December 13, 2022
    Publication date: June 15, 2023
    Applicant: Oracle International Corporation
    Inventors: Cong Duy Vu Hoang, Vishal Vishnoi, Mark Edward Johnson, Thanh Long Duong, Srinivasa Phani Kumar Gadde, Balakota Srinivas Vinnakota
  • 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: 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
  • 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: 11475875
    Abstract: In one aspect, a computerized method useful for implementing a language neutral virtual assistant including the step of providing a language detector. The language detector comprises one or more trained language classifiers. With language detector identifying a language of an incoming message from a user to an artificially intelligent (AI) personal assistant. The method includes the step of receiving an incoming message to the AI personal assistant. The method includes the step of normalizing the incoming message, wherein the normalizing the incoming message comprises a set of spelling corrections and a set of grammar corrections. The method includes the step of translating the incoming message to a specified language with a specified encoding process and a specified decoding process. The method includes the step of providing an AI personal assistant engine that comprise an artificial intelligence which conducts a conversation via auditory or textual methods.
    Type: Grant
    Filed: October 27, 2019
    Date of Patent: October 18, 2022
    Inventors: Sriram Chakravarthy, Madhav Vodnala, Balakota Srinivas Vinnakota, Ram Menon
  • Publication number: 20220100961
    Abstract: Techniques for automatically switching between chatbot skills in the same domain. In one particular aspect, a method is provided that includes receiving an utterance from a user within a chatbot session, where a current skill context is a first skill and a current group context is a first group, inputting the utterance into a candidate skills model for the first group, obtaining, using the candidate skills model, a ranking of skills within the first group, determining, based on the ranking of skills, a second skill is a highest ranked skill, changing the current skill context of the chatbot session to the second skill, inputting the utterance into a candidate flows model for the second skill, obtaining, using the candidate flows model, a ranking of intents within the second skill that match the utterance, and determining, based on the ranking of intents, an intent that is a highest ranked intent.
    Type: Application
    Filed: September 30, 2021
    Publication date: March 31, 2022
    Applicant: Oracle International Corporation
    Inventors: Vishal Vishnoi, Xin Xu, Elias Luqman Jalaluddin, Srinivasa Phani Kumar Gadde, Crystal C. Pan, Mark Edward Johnson, Thanh Long Duong, Balakota Srinivas Vinnakota, Manish Parekh
  • Publication number: 20210304003
    Abstract: Techniques are disclosed for tuning hyperparameters of a model. Datasets are obtained for training the model and metrics are selected for evaluating performance of the model. Each metric is assigned a weight specifying an importance to the performance of the model. A function is created that measures performance based on the weighted metrics. Hyperparameters are tuned to optimize the model performance. Tuning the hyperparameters includes: (i) training the model that is configured based on a current values for the hyperparameters; (ii) evaluating a performance of the model using the function; (iii) determining whether the model is optimized for the metrics; (iv) in response to the model not being optimized, searching for a new values for the hyperparameters, reconfiguring the model with the new values, and repeating steps (i)-(iii) using the reconfigured model; and (v) in response to the model being optimized for the metrics, providing a trained model.
    Type: Application
    Filed: March 29, 2021
    Publication date: September 30, 2021
    Applicant: Oracle International Corporation
    Inventors: Mark Edward Johnson, Thanh Long Duong, Vishal Vishnoi, Balakota Srinivas Vinnakota, Tuyen Quang Pham, Cong Duy Vu Hoang
  • Publication number: 20210303798
    Abstract: The present disclosure relates to techniques for identifying out-of-domain utterances.
    Type: Application
    Filed: March 30, 2021
    Publication date: September 30, 2021
    Applicant: Oracle International Corporation
    Inventors: Thanh Long Duong, Mark Edward Johnson, Vishal Vishnoi, Crystal C. Pan, Vladislav Blinov, Cong Duy Vu Hoang, Elias Luqman Jalaluddin, Duy Vu, Balakota Srinivas Vinnakota
  • Publication number: 20210304075
    Abstract: The present disclosure relates to chatbot systems, and more particularly, to batching techniques for handling unbalanced training data when training a model such that bias is removed from the trained machine learning model when performing inference. In an embodiment, a plurality of raw utterances is obtained. A bias eliminating distribution is determined and a subset of the plurality of raw utterances is batched according to the bias-reducing distribution. The resulting unbiased training data may be input into a prediction model for training the prediction model. The trained prediction model may be obtained and utilized to predict unbiased results from new inputs received by the trained prediction model.
    Type: Application
    Filed: March 30, 2021
    Publication date: September 30, 2021
    Applicant: Oracle International Corporation
    Inventors: Thanh Long Duong, Mark Edward Johnson, Vishal Vishnoi, Balakota Srinivas Vinnakota, Yu-Heng Hong, Elias Luqman Jalaluddin