公开(公告)号：US20250094821A1

公开(公告)日：2025-03-20

申请号：US18885496

申请日：2024-09-13

Applicant: Oracle International Corporation

Inventor： Bhagya Gayathri Hettige ,  Ahmed Ataallah Ataallah Abobakr ,  Vanshika Sridharan ,  Yakupitiyage Don Thanuja Samodhye Dharmasiri ,  Ying Xu ,  Thanh Long Duong ,  Srinivasa Phani Kumar Gadde ,  Vishal Vishnoi

IPC: G06N3/096 ,  G06N3/0475

Abstract: Techniques are disclosed for fine-tuning a pre-trained machine learning model to be used by a digital assistant for supporting a user's interactions. In one aspect, a method includes accessing a set of training examples, generating a set of synthesized training examples using an iterative process including accessing a dialog script and corresponding prompt template and response template for a predefined scenario, generating one or more prompts based on the dialog script and corresponding prompt template, generating one or more responses associated with each of the one or more prompts based on the dialog script and the response template, and linking each of the responses with the associated prompts to generate one or more synthesized training examples in the set of synthesized training examples. The pre-trained machine learning model is then fine-tuned using the set of training examples and the set of synthesized training examples.

公开(公告)号：US20240028963A1

公开(公告)日：2024-01-25

申请号：US18350716

申请日：2023-07-11

Applicant: Oracle International Corporation

Inventor： 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

IPC: G06N20/00

CPC classification number: G06N20/00 ,  G10L15/1815

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.

公开(公告)号：US20230419052A1

公开(公告)日：2023-12-28

申请号：US18163231

申请日：2023-02-01

Applicant: Oracle International Corporation

Inventor： 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

IPC: G06F40/56 ,  G06F40/295 ,  G06F40/247

CPC classification number: G06F40/56 ,  G06F40/247 ,  G06F40/295

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.

公开(公告)号：US20220171947A1

公开(公告)日：2022-06-02

申请号：US17456916

申请日：2021-11-30

Applicant: Oracle International Corporation

Inventor： 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

IPC: G06F40/35 ,  H04L51/02 ,  G06N20/00

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.

公开(公告)号：US12293155B2

公开(公告)日：2025-05-06

申请号：US18630772

申请日：2024-04-09

Applicant: Oracle International Corporation

Inventor： Elias Luqman Jalaluddin ,  Vishal Vishnoi ,  Thanh Long Duong ,  Mark Edward Johnson ,  Poorya Zaremoodi ,  Gautam Singaraju ,  Ying Xu ,  Vladislav Blinov ,  Yu-Heng Hong

IPC: G06F40/289 ,  G06F40/30 ,  G06N3/08 ,  H04L51/02

Abstract: A method 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 including 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.

公开(公告)号：US20250094390A1

公开(公告)日：2025-03-20

申请号：US18885178

申请日：2024-09-13

Applicant: Oracle International Corporation

Inventor： Bhagya Gayathri Hettige ,  Ahmed Ataallah Ataallah Abobakr ,  Vanshika Sridharan ,  Ying Xu ,  Thanh Long Duong ,  Yakupitiyage Don Thanuja Samodhye Dharmasiri ,  Srinivasa Phani Kumar Gadde ,  Vishal Vishnoi ,  Xin Xu

IPC: G06F16/21 ,  G06F16/2453 ,  G06F40/284 ,  G06F40/35

Abstract: Techniques are disclosed herein for routing an utterance to action for a digital assistant with generative artificial intelligence. An input query comprising particular data can be received from a user. An action and a set of input argument slots within a schema associated with the action can be identified based on the input query. The input argument slots can be filled by determining whether one or more parameters are derivable from the particular data and filling the input argument slot with a version of the parameters that conforms to the schema. An execution plan that comprises the action that includes the set of filled input argument sots can be sent to an execution engine configured to execute the action for generating a response to the input query.

公开(公告)号：US12210830B2

公开(公告)日：2025-01-28

申请号：US17750240

申请日：2022-05-20

Applicant: Oracle International Corporation

Inventor： Thanh Tien Vu ,  Tuyen Quang Pham ,  Mark Edward Johnson ,  Thanh Long Duong ,  Ying Xu ,  Poorya Zaremoodi ,  Omid Mohamad Nezami ,  Budhaditya Saha ,  Cong Duy Vu Hoang

IPC: G06F40/30 ,  G06F40/169 ,  G06F40/284 ,  G06F40/295

Abstract: In some aspects, a computing device may receive, at a data processing system, a set of utterances for training or inferencing with a named entity recognizer to assign a label to each token piece from the set of utterances. The computing device may determine a length of each utterance in the set and when the length of the utterance exceeds a pre-determined threshold of token pieces: dividing the utterance into a plurality of overlapping chunks of token pieces; assigning a label together with a confidence score for each token piece in a chunk; determining a final label and an associated confidence score for each chunk of token pieces by merging two confidence scores; determining a final annotated label for the utterance based at least on the merging the two confidence scores; and storing the final annotated label in a memory.

公开(公告)号：US20240256777A1

公开(公告)日：2024-08-01

申请号：US18630772

申请日：2024-04-09

Applicant: Oracle International Corporation

Inventor： Elias Luqman Jalaluddin ,  Vishal Vishnoi ,  Thanh Long Duong ,  Mark Edward Johnson ,  Poorya Zaremoodi ,  Gautam Singaraju ,  Ying Xu ,  Vladislav Blinov ,  Yu-Heng Hong

IPC: G06F40/289 ,  G06F40/30 ,  G06N3/08 ,  H04L51/02

CPC classification number: G06F40/289 ,  G06F40/30 ,  G06N3/08 ,  H04L51/02

Abstract: A method 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 including 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.

公开(公告)号：US20240232541A1

公开(公告)日：2024-07-11

申请号：US18611039

申请日：2024-03-20

Applicant: Oracle International Corporation

Inventor： 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

IPC: G06F40/35 ,  G06F40/205 ,  G06F40/253 ,  G06N3/08 ,  H04L51/02

CPC classification number: G06F40/35 ,  G06N3/08 ,  H04L51/02 ,  G06F40/205 ,  G06F40/253

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.

公开(公告)号：US20230161963A1

公开(公告)日：2023-05-25

申请号：US17750240

申请日：2022-05-20

Applicant: Oracle International Corporation

Inventor： Thanh Tien Vu ,  Tuyen Quang Pham ,  Mark Edward Johnson ,  Thanh Long Duong ,  Ying Xu ,  Poorya Zaremoodi ,  Omid Mohamad Nezami ,  Budhaditya Saha ,  Cong Duy Vu Hoang

IPC: G06F40/295 ,  G06F40/284 ,  G06F40/169

CPC classification number: G06F40/295 ,  G06F40/284 ,  G06F40/169

Abstract: In some aspects, a computing device may receive, at a data processing system, a set of utterances for training or inferencing with a named entity recognizer to assign a label to each token piece from the set of utterances. The computing device may determine a length of each utterance in the set and when the length of the utterance exceeds a pre-determined threshold of token pieces: dividing the utterance into a plurality of overlapping chunks of token pieces; assigning a label together with a confidence score for each token piece in a chunk; determining a final label and an associated confidence score for each chunk of token pieces by merging two confidence scores; determining a final annotated label for the utterance based at least on the merging the two confidence scores; and storing the final annotated label in a memory.