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：US20250095635A1

公开(公告)日：2025-03-20

申请号：US18656274

申请日：2024-05-06

Applicant: Oracle International Corporation

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

IPC: G10L15/06

Abstract: Techniques are disclosed herein for managing ambiguous date mentions in natural language utterances in transforming natural language utterances to logical forms by encoding the uncertainties of the ambiguous date mentions and including the encoded uncertainties in the logical forms. In a training phase, training examples including natural language utterances, logical forms, and database schema information are automatically augmented and used to train a machine learning model to convert natural language utterances to logical form. In an inference phase, input database schema information is augmented and used by the trained machine learning model to convert an input natural language utterance to logical form.

公开(公告)号：US20250094737A1

公开(公告)日：2025-03-20

申请号：US18794986

申请日：2024-08-05

Applicant: Oracle International Corporation

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

IPC: G06F40/58 ,  G06F40/166 ,  G06F40/253 ,  G06F40/295

Abstract: Techniques are disclosed herein for managing date-time intervals in transforming natural language utterances to logical forms by providing an enhanced grammar, a natural language utterance comprising a date-time interval, and database schema information to a machine learning model that has been trained to convert natural language utterances to logical forms; and using the machine learning model to convert the natural language utterance to an output logical form, wherein the output logical form comprises at least one of the date-time interval and an extraction function for extracting date-time information corresponding to the date-time interval from at least one date-time attribute of the database schema information.

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.