Patents by Inventor Poorya Zaremoodi

Poorya Zaremoodi 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: 20240232187
    Abstract: The present disclosure is related to techniques for converting a natural language utterance to a logical form query and deriving a natural language interpretation of the logical form query. The techniques include accessing a Meaning Resource Language (MRL) query and converting the MRL query into a MRL structure including logical form statements. The converting includes extracting operations and associated attributes from the MRL query and generating the logical form statements from the operations and associated attributes. The techniques further include translating each of the logical form statements into a natural language expression based on a grammar data structure that includes a set of rules for translating logical form statements into corresponding natural language expressions, combining the natural language expressions into a single natural language expression, and providing the single natural language expression as an interpretation of the natural language utterance.
    Type: Application
    Filed: May 22, 2023
    Publication date: July 11, 2024
    Applicant: Oracle International Corporation
    Inventors: Chang Xu, Poorya Zaremoodi, Cong Duy Vu Hoang, Nitika Mathur, Philip Arthur, Steve Wai-Chun Siu, Aashna Devang Kanuga, Gioacchino Tangari, Mark Edward Johnson, Thanh Long Duong, Vishal Vishnoi, Stephen Andrew McRitchie, Christopher Mark Broadbent
  • Publication number: 20240232541
    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: March 20, 2024
    Publication date: July 11, 2024
    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
  • Patent number: 12026468
    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: Grant
    Filed: October 28, 2021
    Date of Patent: July 2, 2024
    Assignee: 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
  • Patent number: 12019994
    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: Grant
    Filed: November 30, 2021
    Date of Patent: June 25, 2024
    Assignee: 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: 20240169161
    Abstract: Obtaining collections of sentences in different languages that are usable for training models in various applications of artificial intelligence is provided. A method is provided that obtains, from text corpus, webpages in a plurality of languages, each of the webpages corresponding to an URL; obtains annotations for each of the webpages based on its URL, to obtain annotated data entries corresponding to the webpages, each of the annotated data entries including a classification label corresponding to a sub-topic of one of a plurality of topics, where each of the plurality of topics includes a corresponding plurality of sub-topics; filters the annotated data entries to obtain topic-specific content in a target language based on the classification labels, the topic-specific content corresponding to one or more sub-topics; performs post-processing on the topic-specific content to obtain result data; and outputs the result data for the topic.
    Type: Application
    Filed: August 21, 2023
    Publication date: May 23, 2024
    Applicant: Oracle International Corporation
    Inventors: Paria Jamshid Lou, Gioacchino Tangari, Jason Black, Bhagya Gayathri Hettige, Xu Zhong, Poorya Zaremoodi, Thanh Long Duong, Mark Edward Johnson
  • Publication number: 20240143934
    Abstract: A method includes accessing document including sentences, document being associated with configuration flag indicating whether ABSA, SLSA, or both are to be performed; inputting the document into language model that generates chunks of token embeddings for the document; and, based on the configuration flag, performing at least one from among the ABSA and the SLSA by inputting the chunks of token embeddings into a multi-task model. When performing the SLSA, a part of token embeddings in each of the chunks is masked, and the masked token embeddings do not belong to a particular sentence on which the SLSA is performed.
    Type: Application
    Filed: October 12, 2023
    Publication date: May 2, 2024
    Applicant: Oracle International Corporation
    Inventors: Poorya Zaremoodi, Duy Vu, Nagaraj N. Bhat, Srijon Sarkar, Varsha Kuppur Rajendra, Thanh Long Duong, Mark Edward Johnson, Pramir Sarkar, Shahid Reza
  • Patent number: 11972220
    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: Grant
    Filed: November 29, 2021
    Date of Patent: April 30, 2024
    Assignee: 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: 20240135116
    Abstract: A computer-implemented method includes: accessing a plurality of datasets, where each dataset of the plurality of datasets includes training examples; selecting datasets that include the training examples in a source language and a target language; and sampling, based on a sampling weight that is determined for each of the selected datasets, the training examples from the selected datasets to generate the training batches; training an ML model for performing at least a first task using the training examples of the training batches, by interleavingly inputting the training batches to the ML model; and outputting the trained ML model configured to perform the at least the first task on input utterances provided in at least one among the source language and the target language. The sampling weight is determined for each of the selected datasets based on one or more attributes common to the training examples of the selected dataset.
    Type: Application
    Filed: October 12, 2023
    Publication date: April 25, 2024
    Applicant: Oracle International Corporation
    Inventors: Duy Vu, Poorya Zaremoodi, Nagaraj N. Bhat, Srijon Sarkar, Varsha Kuppur Rajendra, Thanh Long Duong, Mark Edward Johnson, Pramir Sarkar, Shahid Reza
  • Publication number: 20240134850
    Abstract: The present disclosure is related to techniques for converting a natural language utterance to a logical form query and deriving a natural language interpretation of the logical form query. The techniques include accessing a Meaning Resource Language (MRL) query and converting the MRL query into a MRL structure including logical form statements. The converting includes extracting operations and associated attributes from the MRL query and generating the logical form statements from the operations and associated attributes. The techniques further include translating each of the logical form statements into a natural language expression based on a grammar data structure that includes a set of rules for translating logical form statements into corresponding natural language expressions, combining the natural language expressions into a single natural language expression, and providing the single natural language expression as an interpretation of the natural language utterance.
    Type: Application
    Filed: May 21, 2023
    Publication date: April 25, 2024
    Applicant: Oracle International Corporation
    Inventors: Chang Xu, Poorya Zaremoodi, Cong Duy Vu Hoang, Nitika Mathur, Philip Arthur, Steve Wai-Chun Siu, Aashna Devang Kanuga, Gioacchino Tangari, Mark Edward Johnson, Thanh Long Duong, Vishal Vishnoi, Stephen Andrew McRitchie, Christopher Mark Broadbent
  • Publication number: 20240126999
    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: December 19, 2023
    Publication date: April 18, 2024
    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: 20240095584
    Abstract: Techniques are disclosed herein for objective function optimization in target based hyperparameter tuning. In one aspect, a computer-implemented method is provided that includes initializing a machine learning algorithm with a set of hyperparameter values and obtaining a hyperparameter objective function that comprises a domain score for each domain that is calculated based on a number of instances within an evaluation dataset that are correctly or incorrectly predicted by the machine learning algorithm during a given trial. For each trial of a hyperparameter tuning process: training the machine learning algorithm to generate a machine learning model, running the machine learning model in different domains using the set of hyperparameter values, evaluating the machine learning model for each domain, and once the machine learning model has reached convergence, outputting at least one machine learning model.
    Type: Application
    Filed: May 15, 2023
    Publication date: March 21, 2024
    Applicant: Oracle International Corporation
    Inventors: Ying Xu, Vladislav Blinov, Ahmed Ataallah Ataallah Abobakr, Thanh Long Duong, Mark Edward Johnson, Elias Luqman Jalaluddin, Xin Xu, Srinivasa Phani Kumar Gadde, Vishal Vishnoi, Poorya Zaremoodi, Umanga Bista
  • Publication number: 20240061834
    Abstract: Systems and methods identify whether an input utterance is suitable for providing to a machine learning model configured to generate a query for a database. Techniques include generating an input string by concatenating a natural language utterance with a database schema representation for a database; providing the input string to a first machine learning model; based on the input string, generating, by the first machine learning model, a score indicating whether the natural language utterance is translatable to a database query for the database and should be routed to a second machine learning model, the second machine learning model configured to generate a query for the database based on the natural language utterance; comparing the score to a threshold value; and responsive to determining that the score exceeds the threshold value, providing the natural language utterance or the input string to the second machine learning model.
    Type: Application
    Filed: August 21, 2023
    Publication date: February 22, 2024
    Applicant: Oracle International Corporation
    Inventors: Gioacchino Tangari, Cong Duy Vu Hoang, Poorya Zaremoodi, Philip Arthur, Nitika Mathur, Mark Edward Johnson, Thanh Long Duong
  • Publication number: 20240061833
    Abstract: Techniques are disclosed for augmenting training data for training a machine learning model to generate database queries. Training data comprising a first training example comprising a first natural language utterance, a logical form for the first natural language utterance, and associated first metadata is obtained. From the first training example, a template utterance is generated. A second natural language utterance is generated by filling slots in the template utterance based on a database schema and database values. Updated metadata is produced based on the first metadata and the second natural language utterance. A second training example is generated, comprising the second natural language utterance, the logical form for the first natural language utterance, and the updated metadata. The training data is augmented by adding the second training example. A machine learning model is trained to generate a database query comprising the database operation using the augmented training data set.
    Type: Application
    Filed: July 5, 2023
    Publication date: February 22, 2024
    Applicant: Oracle International Corporation
    Inventors: Gioacchino Tangari, Nitika Mathur, Philip Arthur, Cong Duy Vu Hoang, Aashna Devang Kanuga, Steve Wai-Chun Siu, Syed Najam Abbas Zaidi, Poorya Zaremoodi, Thanh Long Duong, Mark Edward Johnson
  • Publication number: 20240061832
    Abstract: Techniques are disclosed herein for converting a natural language utterance to an intermediate database query representation. An input string is generated by concatenating a natural language utterance with a database schema representation for a database. Based on the input string, a first encoder generates one or more embeddings of the natural language utterance and the database schema representation. A second encoder encodes relations between elements in the database schema representation and words in the natural language utterance based on the one or more embeddings. A grammar-based decoder generates an intermediate database query representation based on the encoded relations and the one or more embeddings. Based on the intermediate database query representation and an interface specification, a database query is generated in a database query language.
    Type: Application
    Filed: June 14, 2023
    Publication date: February 22, 2024
    Applicant: Oracle International Corporation
    Inventors: Cong Duy Vu Hoang, Stephen Andrew McRitchie, Mark Edward Johnson, Shivashankar Subramanian, Aashna Devang Kanuga, Nitika Mathur, Gioacchino Tangari, Steve Wai-Chun Siu, Poorya Zaremoodi, Vasisht Raghavendra, Thanh Long Duong, Srinivasa Phani Kumar Gadde, Vishal Vishnoi, Christopher Mark Broadbent, Philip Arthur, Syed Najam Abbas Zaidi
  • Publication number: 20240061835
    Abstract: Systems and methods fine-tune a pretrained machine learning model. For a model having multiple layers, an initial set of configurations is identified, each configuration establishing layers to be frozen and layers to be fine-tuned. A configuration that is optimized with respect to one or more parameters is selected, establishing a set of fine-tuning layers and a set of frozen layers. An input for the model is provided to a remote system. An output of the set of frozen layers of the model, given the provided input, is received back and locally stored. The set of fine-tuning layers of the model is loaded from the remote system. The model is fine-tuned by retrieving the locally stored output of the set of frozen layers, and updating weights associated with the set of fine-tuning layers of the machine learning model.
    Type: Application
    Filed: August 21, 2023
    Publication date: February 22, 2024
    Applicant: Oracle International Corporation
    Inventors: Shivashankar Subramanian, Gioacchino Tangari, Thanh Tien Vu, Cong Duy Vu Hoang, Poorya Zaremoodi, Dalu Guo, Mark Edward Johnson, Thanh Long Duong
  • Publication number: 20240062011
    Abstract: Techniques are disclosed herein for using named entity recognition to resolve entity expression while transforming natural language to a meaning representation language. In one aspect, a method includes accessing natural language text, predicting, by a first machine learning model, a class label for a token in the natural language text, predicting, by a second machine-learning model, operators for a meaning representation language and a value or value span for each attribute of the operators, in response to determining that the value or value span for a particular attribute matches the class label, converting a portion of the natural language text for the value or value span into a resolved format, and outputting syntax for the meaning representation language. The syntax comprises the operators with the portion of the natural language text for the value or value span in the resolved format.
    Type: Application
    Filed: July 13, 2023
    Publication date: February 22, 2024
    Applicant: Oracle International Corporation
    Inventors: Aashna Devang Kanuga, Cong Duy Vu Hoang, Mark Edward Johnson, Vasisht Raghavendra, Yuanxu Wu, Steve Wai-Chun Siu, Nitika Mathur, Gioacchino Tangari, Shubham Pawankumar Shah, Vanshika Sridharan, Zikai Li, Diego Andres Cornejo Barra, Stephen Andrew McRitchie, Christopher Mark Broadbent, Vishal Vishnoi, Srinivasa Phani Kumar Gadde, Poorya Zaremoodi, Thanh Long Duong, Bhagya Gayathri Hettige, Tuyen Quang Pham, Arash Shamaei, Thanh Tien Vu, Yakupitiyage Don Thanuja Samodhve Dharmasiri
  • Publication number: 20240062021
    Abstract: Techniques are disclosed herein for calibrating confidence scores of a machine learning model trained to translate natural language to a meaning representation language. The techniques include obtaining one or more raw beam scores generated from one or more beam levels of a decoder of a machine learning model trained to translate natural language to a logical form, where each of the one or more raw beam scores is a conditional probability of a sub-tree determined by a heuristic search algorithm of the decoder at one of the one or more beam levels, classifying, by a calibration model, a logical form output by the machine learning model as correct or incorrect based on the one or more raw beam scores, and providing the logical form with a confidence score that is determined based on the classifying of the logical form.
    Type: Application
    Filed: February 9, 2023
    Publication date: February 22, 2024
    Applicant: Oracle International Corporation
    Inventors: Gioacchino Tangari, Cong Duy Vu Hoang, Mark Edward Johnson, Poorya Zaremoodi, Nitika Mathur, Aashna Devang Kanuga, Thanh Long Duong
  • Publication number: 20230153528
    Abstract: Techniques for augmentation and batch balancing of training data to enhance negation and fairness of a machine learning model. In one particular aspect, a method is provided that includes generating a list of demographic words associated with a demographic group, searching an unlabeled corpus of text to identify unlabeled examples in a target domain comprising at least one demographic word from the list of demographic words, rewriting the unlabeled examples to create one or more versions of each of the unlabeled examples and generate a fairness invariance data set, and training the machine learning model using unlabeled examples from the fairness invariance data set.
    Type: Application
    Filed: November 10, 2022
    Publication date: May 18, 2023
    Applicant: Oracle International Corporation
    Inventors: Duy Vu, Varsha Kuppur Rajendra, Dai Hoang Tran, Shivashankar Subramanian, Poorya Zaremoodi, Thanh Long Duong, Mark Edward Johnson
  • Publication number: 20230153688
    Abstract: Techniques for augmentation and batch balancing of training data to enhance negation and fairness of a machine learning model. In one particular aspect, a method is provided that includes obtaining a training set of labeled examples for training a machine learning model to classify sentiment, searching the training set of labeled examples or an unlabeled corpus of text on target domains for sentiment examples having negation cues, sentiment laden words, words with sentiment prefixes or suffixes, or a combination thereof, rewriting the sentiment examples to create negated versions thereof and generate a labeled negation pair data set, and training the machine learning model using labeled examples from the labeled negation pair data set.
    Type: Application
    Filed: November 10, 2022
    Publication date: May 18, 2023
    Applicant: Oracle International Corporation
    Inventors: Duy Vu, Varsha Kuppur Rajendra, Dai Hoang Tran, Shivashankar Subramanian, Poorya Zaremoodi, Thanh Long Duong, Mark Edward Johnson
  • 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