Patents by Inventor Elias Luqman Jalaluddin

Elias Luqman Jalaluddin 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: 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
  • 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
  • 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: 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: 20240086767
    Abstract: Techniques are disclosed herein for continuous hyperparameter tuning with automatic domain weight adjustment based on periodic performance checkpoints. In one aspect, a method is provided that includes initializing a machine learning algorithm with a set of hyperparameter values and obtaining a hyperparameter objective function that is defined at least in part on a plurality of domains of a search space that is associated with the machine learning algorithm. For each trial of a hyperparameter tuning process: running the machine learning algorithm in different domains using the set of hyperparameter values, periodically checking a performance of the machine learning algorithm in the different domains based on the hyperparameter objective function; and continuing hyperparameter tuning with a new set of hyperparameter values after automatically adjusting the domain weights according to a regression status of the different domains.
    Type: Application
    Filed: April 3, 2023
    Publication date: March 14, 2024
    Applicant: Oracle International Corporation
    Inventors: Ying Xu, Vladislav Blinov, Ahmed Ataallah Ataallah Abobakr, Mark Edward Johnson, Thanh Long Duong, Srinivasa Phani Kumar Gadde, Vishal Vishnoi, Xin Xu, Elias Luqman Jalaluddin, Umanga Bista
  • 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: 20240028963
    Abstract: An augmentation and feature caching subsystem is described for training AI/ML models. In one particular aspect, a method is provided that includes receiving data comprising training examples, one or more augmentation configuration hyperparameters and one or more feature extraction configuration hyperparameters; generating a first key based on one of the training examples and the one or more augmentation configuration hyperparameters; searching a first key-value storage based on the first key; obtaining one or more augmentations based on the search of the first key-value storage; applying the obtained one or more augmentations to the training examples to result in augmented training examples; generating a second key based on one of the augmented training examples and the one or more feature extraction configuration hyperparameters; searching a second key-value storage based on the second key; obtaining one or more features based on the search of the second key-value storage.
    Type: Application
    Filed: July 11, 2023
    Publication date: January 25, 2024
    Applicant: Oracle International Corporation
    Inventors: Vladislav Blinov, Vishal Vishnoi, Thanh Long Duong, Mark Edward Johnson, Xin Xu, Elias Luqman Jalaluddin, Ying Xu, Ahmed Ataallah Ataallah Abobakr, Umanga Bista, Thanh Tien Vu
  • Publication number: 20240013780
    Abstract: Techniques for data augmentation for training chatbot systems in natural language processing. In one particular aspect, a method is provided that includes generating a list of values to cover for an entity, selecting utterances from a set of data that have context for the entity, converting the utterances into templates, where each template of the templates comprises a slot that maps to the list of values for the entity, selecting a template from the templates, selecting a value from the list of values based on the mapping between the slot within the selected template and the list of values for the entity; and creating an artificial utterance based on the selected template and the selected value, where the creating the artificial utterance comprises inserting the selected value into the slot of the selected template that maps to the list of values for the entity.
    Type: Application
    Filed: September 21, 2023
    Publication date: January 11, 2024
    Applicant: Oracle International Corporation
    Inventors: Srinivasa Phani Kumar Gadde, Yuanxu Wu, Aashna Devang Kanuga, Elias Luqman Jalaluddin, Vishal Vishnoi, Mark Edward Johnson
  • Publication number: 20230419040
    Abstract: Novel techniques are described for data augmentation using a two-stage entity-aware augmentation to improve model robustness to entity value changes for intent prediction.
    Type: Application
    Filed: February 1, 2023
    Publication date: December 28, 2023
    Applicant: Oracle International Corporation
    Inventors: Ahmed Ataallah Ataallah Abobakr, Shivashankar Subramanian, Ying Xu, Vladislav Blinov, Umanga Bista, Tuyen Quang Pham, Thanh Long Duong, Mark Edward Johnson, Elias Luqman Jalaluddin, Vanshika Sridharan, Xin Xu, Srinivasa Phani Kumar Gadde, Vishal Vishnoi
  • Publication number: 20230419127
    Abstract: Novel techniques are described for negative entity-aware augmentation using a two-stage augmentation to improve the stability of the model to entity value changes for intent prediction. In some embodiments, a method comprises accessing a first set of training data for an intent prediction model, the first set of training data comprising utterances and intent labels; applying one or more negative entity-aware data augmentation techniques to the first set of training data, depending on the tuning requirements for hyper-parameters, to result in a second set of training data, where the one or more negative entity-aware data augmentation techniques comprise Keyword Augmentation Technique (“KAT”) plus entity without context technique and KAT plus entity in random context as OOD technique; combining the first set of training data and the second set of training data to generate expanded training data; and training the intent prediction model using the expanded training data.
    Type: Application
    Filed: February 1, 2023
    Publication date: December 28, 2023
    Applicant: Oracle International Corporation
    Inventors: Ahmed Ataallah Ataallah Abobakr, Shivashankar Subramanian, Ying Xu, Vladislav Blinov, Umanga Bista, Tuyen Quang Pham, Thanh Long Duong, Mark Edward Johnson, Elias Luqman Jalaluddin, Vanshika Sridharan, Xin Xu, Srinivasa Phani Kumar Gadde, Vishal Vishnoi
  • Publication number: 20230419052
    Abstract: Novel techniques are described for positive entity-aware augmentation using a two-stage augmentation to improve the stability of the model to entity value changes for intent prediction. In one particular aspect, a method is provided that includes accessing a first set of training data for an intent prediction model, the first set of training data comprising utterances and intent labels; applying one or more positive data augmentation techniques to the first set of training data, depending on the tuning requirements for hyper-parameters, to result in a second set of training data, where the positive data augmentation techniques comprise Entity-Aware (“EA”) technique and a two-stage augmentation technique; combining the first set of training data and the second set of training data to generate expanded training data; and training the intent prediction model using the expanded training data.
    Type: Application
    Filed: February 1, 2023
    Publication date: December 28, 2023
    Applicant: Oracle International Corporation
    Inventors: Ahmed Ataallah Ataallah Abobakr, Shivashankar Subramanian, Ying Xu, Vladislav Blinov, Umanga Bista, Tuyen Quang Pham, Thanh Long Duong, Mark Edward Johnson, Elias Luqman Jalaluddin, Vanshika Sridharan, Xin XU, Srinivasa Phani Kumar Gadde, Vishal Vishnoi
  • 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
  • Publication number: 20230376700
    Abstract: Techniques are provided for generating training data to facilitate fine-tuning embedding models. Training data including anchor utterances is obtained. Positive utterances and negative utterances are generated from the anchor utterances. Tuples including the anchor utterances, the positive utterances, and the negative utterances are formed. Embeddings for the tuples are generated and a pre-trained embedding model is fine-tuned based on the embeddings. The fine-tuned model can be deployed to a system.
    Type: Application
    Filed: May 9, 2023
    Publication date: November 23, 2023
    Applicant: Oracle International Corporation
    Inventors: Umanga Bista, Vladislav Blinov, Mark Edward Johnson, Ahmed Ataallah Ataallah Abobakr, Thanh Long Duong, Srinivasa Phani Kumar Gadde, Vishal Vishnoi, Elias Luqman Jalaluddin, Xin Xu, Shivashankar Subramanian
  • Patent number: 11804219
    Abstract: Techniques for data augmentation for training chatbot systems in natural language processing. In one particular aspect, a method is provided that includes generating a list of values to cover for an entity, selecting utterances from a set of data that have context for the entity, converting the utterances into templates, where each template of the templates comprises a slot that maps to the list of values for the entity, selecting a template from the templates, selecting a value from the list of values based on the mapping between the slot within the selected template and the list of values for the entity; and creating an artificial utterance based on the selected template and the selected value, where the creating the artificial utterance comprises inserting the selected value into the slot of the selected template that maps to the list of values for the entity.
    Type: Grant
    Filed: June 11, 2021
    Date of Patent: October 31, 2023
    Assignee: Oracle International Corporation
    Inventors: Srinivasa Phani Kumar Gadde, Yuanxu Wu, Aashna Devang Kanuga, Elias Luqman Jalaluddin, Vishal Vishnoi, Mark Edward Johnson
  • 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