公开(公告)号：US20250156649A1

公开(公告)日：2025-05-15

申请号：US18505498

申请日：2023-11-09

Applicant: Oracle International Corporation

Inventor： Gioacchino Tangari ,  Chang Xu ,  Nitika Mathur ,  Philip Arthur ,  Syed Najam Abbas Zaidi ,  Aashna Devang Kanuga ,  Cong Duy Vu Hoang ,  Poorya Zaremoodi ,  Thanh Long Duong ,  Mark Edward Johnson ,  Vishal Vishnoi

IPC: G06F40/40 ,  G06F40/211 ,  G06F40/284

Abstract: Techniques are disclosed herein for improving model robustness on operators and triggering keywords in natural language to a meaning representation language system. The techniques include augmenting an original set of training data for a target robustness bucket by leveraging a combination of two training data generation techniques: (1) modification of existing training examples and (2) synthetic template-based example generation. The resulting set of augmented data examples from the two training data generation techniques are appended to the original set of training data to generate an augmented training data set and the augmented training data set is used to train a machine learning model to generate logical forms for utterances.

公开(公告)号：US20250068627A1

公开(公告)日：2025-02-27

申请号：US18616801

申请日：2024-03-26

Applicant: Oracle International Corporation

Inventor： Cong Duy Vu Hoang ,  Gioacchino Tangari ,  Stephen Andrew McRitchie ,  Nitika Mathur ,  Aashna Devang Kanuga ,  Steve Wai-Chun Siu ,  Dalu Guo ,  Chang Xu ,  Mark Edward Johnson ,  Christopher Mark Broadbent ,  Thanh Long Duong ,  Srinivasa Phani Kumar Gadde ,  Vishal Vishnoi ,  Chandan Basavaraju ,  Kenneth Khiaw Hong Eng

IPC: G06F16/2452 ,  G06F16/2457 ,  G06F16/28

Abstract: Techniques are disclosed herein for transforming natural language conversations into a visual output. In one aspect, a computer-implement method includes generating an input string by concatenating a natural language utterance with a schema representation comprising a set of entities for visualization actions, generating, by a first encoder of a machine learning model, one or more embeddings of the input string, encoding, by a second encoder of the machine learning model, relations between elements in the schema representation and words in the natural language utterance based on the one or more embeddings, generating, by a grammar-based decoder of the machine learning model and based on the encoded relations and the one or more embeddings, an intermediate logical form that represents at least the query, the one or more visualization actions, or the combination thereof, and generating, based on the intermediate logical form, a command for a computing system.

公开(公告)号：US20240134850A1

公开(公告)日：2024-04-25

申请号：US18321144

申请日：2023-05-21

Applicant: Oracle International Corporation

Inventor： 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

IPC: G06F16/2452 ,  G06F40/211 ,  G06F40/30

CPC classification number: G06F16/24522 ,  G06F40/211 ,  G06F40/30

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.

公开(公告)号：US20240232187A9

公开(公告)日：2024-07-11

申请号：US18321144

申请日：2023-05-22

Applicant: Oracle International Corporation

Inventor： 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

IPC: G06F16/2452 ,  G06F40/211 ,  G06F40/30

CPC classification number: G06F16/24522 ,  G06F40/211 ,  G06F40/30

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.

公开(公告)号：US20250118398A1

公开(公告)日：2025-04-10

申请号：US18884459

申请日：2024-09-13

Applicant: Oracle International Corporation

Inventor： Shubham Pawankumar Shah ,  Syed Najam Abbas Zaidi ,  Xu Zhong ,  Poorya Zaremoodi ,  Srinivasa Phani Kumar Gadde ,  Arash Shamaei ,  Ganesh Kumar ,  Thanh Tien Vu ,  Nitika Mathur ,  Chang Xu ,  Shiquan Yang ,  Sagar Kalyan Gollamudi

IPC: G16H10/60 ,  G06N20/00

Abstract: Techniques are disclosed for automatically generating Subjective, Objective, Assessment and Plan (SOAP) notes. Particularly, techniques are disclosed for training data collection and evaluation for automatic SOAP note generation. Training data is accessed, and evaluation process is performed on the training data to result in evaluated training data. A fine-tuned machine-learning model is generated using the evaluated training data. The fine-tuned machine-learning model can be used to perform a task associated with generating a SOAP note.

公开(公告)号：US20250068626A1

公开(公告)日：2025-02-27

申请号：US18593316

申请日：2024-03-01

Applicant: Oracle International Corporation

Inventor： Gioacchino Tangari ,  Steve Wai-Chun Siu ,  Dalu Guo ,  Cong Duy Vu Hoang ,  Berk Sarioz ,  Chang Xu ,  Stephen Andrew McRitchie ,  Mark Edward Johnson ,  Christopher Mark Broadbent ,  Thanh Long Duong ,  Srinivasa Phani Kumar Gadde ,  Vishal Vishnoi ,  Chandan Basavaraju ,  Kenneth Khiaw Hong Eng

IPC: G06F16/2452 ,  G06F16/28

Abstract: The present disclosure relates to manufacturing training data by leveraging an automated pipeline that manufactures visualization training datasets to train a machine learning model to convert a natural language utterance into meaning representation language logical form that includes one or more visualization actions. Aspects are directed towards accessing an original training dataset, a visualization query dataset, an incremental visualization dataset, a manipulation visualization dataset, or any combination thereof. One or more visualization training datasets are generated by: (i) modifying examples in the original training dataset, the visualization query dataset, or both to include visualization actions, (ii) generating examples, using the incremental visualization dataset, the manipulation visualization dataset, or both, that include visualization actions, or (iii) both (i) and (ii). An augmented training dataset is generated by adding the one or more visualization training datasets to the original training dataset and then used to train the machine learning model.