公开(公告)号：US20250094465A1

公开(公告)日：2025-03-20

申请号：US18825573

申请日：2024-09-05

Applicant: Oracle International Corporation

Inventor： Xin Xu ,  Bhagya Gayathri Hettige ,  Srinivasa Phani Kumar Gadde ,  Yakupitiyage Don Thanuja Samodhye Dharmasiri ,  Vanshika Sridharan ,  Vishal Vishnoi ,  Mark Edward Johnson

IPC: G06F16/33 ,  G06F16/383

Abstract: Techniques are disclosed herein for executing an execution plan for a digital assistant with generative artificial intelligence (genAI). A first genAI model can generate a list of executable actions based on an utterance provided by a user. An execution plan can be generated to include the executable actions. The execution plan can be executed by performing an iterative process for each of the executable actions. The iterative process can include identifying an action type, invoking one or more states, and executing, by the one or more states, the executable action using an asset to obtain an output. A second prompt can be generated based on the output obtained from executing each of the executable actions. A second genAI model can generate a response to the utterance based on the second prompt.

公开(公告)号：US20240289555A1

公开(公告)日：2024-08-29

申请号：US18659606

申请日：2024-05-09

Applicant: Oracle International Corporation

Inventor： 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

IPC: G06F40/30 ,  G06F40/205 ,  G06F40/289 ,  G06N20/00 ,  H04L51/02

CPC classification number: G06F40/30 ,  G06F40/289 ,  G06N20/00 ,  H04L51/02 ,  G06F40/205

Abstract: The present disclosure relates to techniques for identifying out-of-domain utterances. One particular technique includes receiving an utterance and a target domain of a chatbot, generating a sentence embedding for the utterance, obtaining an embedding representation for each cluster of in-domain utterances associated with the target domain, predicting, using a metric learning model, a first probability that the utterance belongs to the target domain based on a similarity or difference between the sentence embedding and each embedding representation for each cluster, predicting, using an outlier detection model, a second probability that the utterance belongs to the target domain based on a determined distance or density deviation between the sentence embedding and embedding representations for neighboring clusters, evaluating the first probability and the second probability to determine a final probability, and classifying the utterance as in-domain or out-of-domain for the chatbot based on the final probability.

公开(公告)号：US20240169155A1

公开(公告)日：2024-05-23

申请号：US18424178

申请日：2024-01-26

Applicant: Oracle International Corporation

Inventor： 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

IPC: G06F40/295 ,  G06F40/211 ,  G06F40/35 ,  G06F40/56 ,  G06N5/043

CPC classification number: G06F40/295 ,  G06F40/211 ,  G06F40/35 ,  G06F40/56 ,  G06N5/043

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.

公开(公告)号：US11922123B2

公开(公告)日：2024-03-05

申请号：US17490792

申请日：2021-09-30

Applicant: Oracle International Corporation

Inventor： 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

IPC: G06F40/295 ,  G06F40/211 ,  G06F40/35 ,  G06F40/56 ,  G06N5/043

CPC classification number: G06F40/295 ,  G06F40/211 ,  G06F40/35 ,  G06F40/56 ,  G06N5/043

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.

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：US11790901B2

公开(公告)日：2023-10-17

申请号：US18092170

申请日：2022-12-30

Applicant: Oracle International Corporation

Inventor： Thanh Long Duong ,  Mark Edward Johnson ,  Vu Cong Duy Hoang ,  Tuyen Quang Pham ,  Yu-Heng Hong ,  Vladislavs Dovgalecs ,  Guy Bashkansky ,  Jason Eric Black ,  Andrew David Bleeker ,  Serge Le Huitouze

IPC: G10L15/22

CPC classification number: G10L15/22

Abstract: Described herein are dialog systems, and techniques for providing such dialog systems, that are suitable for use on standalone computing devices. In some embodiments, a dialog system includes a dialog manager, which takes as input an input logical form, which may be a representation of user input. The dialog manager may include a dialog state tracker, an execution subsystem, a dialog policy subsystem, and a context stack. The dialog state tracker may generate an intermediate logical form from the input logical form combined with a context from the context stack. The context stack may maintain a history of a current dialog, and thus, the intermediate logical form may include contextual information potentially missing from the input logical form. The execution subsystem may execute the intermediate logical form to produce an execution result, and the dialog policy subsystem may generate an output logical form based on the execution result.

公开(公告)号：US20230186914A1

公开(公告)日：2023-06-15

申请号：US18092170

申请日：2022-12-30

Applicant: Oracle International Corporation

Inventor： Thanh Long Duong ,  Mark Edward Johnson ,  Vu Cong Duy Hoang ,  Tuyen Quang Pham ,  Yu-Heng Hong ,  Vladislavs Dovgalecs ,  Guy Bashkansky ,  Jason Eric Black ,  Andrew David Bleeker ,  Serge Le Huitouze

IPC: G10L15/22

CPC classification number: G10L15/22

Abstract: Described herein are dialog systems, and techniques for providing such dialog systems, that are suitable for use on standalone computing devices. In some embodiments, a dialog system includes a dialog manager, which takes as input an input logical form, which may be a representation of user input. The dialog manager may include a dialog state tracker, an execution subsystem, a dialog policy subsystem, and a context stack. The dialog state tracker may generate an intermediate logical form from the input logical form combined with a context from the context stack. The context stack may maintain a history of a current dialog, and thus, the intermediate logical form may include contextual information potentially missing from the input logical form. The execution subsystem may execute the intermediate logical form to produce an execution result, and the dialog policy subsystem may generate an output logical form based on the execution result.

公开(公告)号：US20230169955A1

公开(公告)日：2023-06-01

申请号：US17993130

申请日：2022-11-23

Applicant: Oracle International Corporation

Inventor： Elias Luqman Jalaluddin ,  Vishal Vishnoi ,  Mark Edward Johnson ,  Thanh Long Duong ,  Yu-Heng Hong ,  Balakota Srinivas Vinnakota

IPC: G10L15/06 ,  G10L15/05 ,  G10L15/18 ,  G10L15/22 ,  G10L15/26

CPC classification number: G10L15/063 ,  G10L15/05 ,  G10L15/18 ,  G10L15/22 ,  G10L15/26 ,  G10L2015/0633 ,  G10L2015/0638 ,  G10L2015/227

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.

公开(公告)号：US20230153687A1

公开(公告)日：2023-05-18

申请号：US17984717

申请日：2022-11-10

Applicant: Oracle International Corporation

Inventor： Duy Vu ,  Varsha Kuppur Rajendra ,  Shivashankar Subramanian ,  Ahmed Ataallah Ataallah Abobakr ,  Thanh Long Duong ,  Mark Edward Johnson

IPC: G06N20/00 ,  G06K9/62

CPC classification number: G06N20/00 ,  G06K9/6259 ,  G06K9/6262

Abstract: Techniques for named entity bias detection and mitigation for sentence sentiment analysis. 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, preparing a list of named entities using one or more data sources, for each example in the training set of labeled examples with a named entity, replacing the named entity with a corresponding entity type tag to generate a labeled template data set, executing a sampling process for each entity type t within the labeled template data set to generate a augmented invariance data set comprising one or more invariance groups having labeled examples for each entity type t, and training the machine learning model using labeled examples from the augmented invariance data set.