公开(公告)号：US20250095806A1

公开(公告)日：2025-03-20

申请号：US18883595

申请日：2024-09-12

Applicant: Oracle International Corporation

Inventor： Syed Najam Abbas Zaidi ,  Shiquan Yang ,  Poorya Zaremoodi ,  Nitika Mathur ,  Shubham Pawankumar Shah ,  Arash Shamaei ,  Sagar Kalyan Gollamudi

IPC: G16H10/60 ,  G06N20/20

Abstract: Techniques are disclosed for automatically generating Subjective, Objective, Assessment and Plan (SOAP) notes. Particularly, techniques are disclosed for identifying entities for automatic SOAP note generation. A text transcript is accessed and segmented into portions. The text transcript can correspond to an interaction between a first entity and a second entity. Entities for the respective portions are identified using machine-learning models. A SOAP note is generated using the one or more machine-learning models and facts are derived from the text transcript based at least in-part on the entities. The SOAP note can be stored in a database in association with at least one of the first entity and the second entity.

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：US20230186161A1

公开(公告)日：2023-06-15

申请号：US18065422

申请日：2022-12-13

Applicant: Oracle International Corporation

Inventor： Philip Arthur ,  Vishal Vishnoi ,  Mark Edward Johnson ,  Thanh Long Duong ,  Srinivasa Phani Kumar Gadde ,  Balakota Srinivas Vinnakota ,  Cong Duy Vu Hoang ,  Steve Wai-Chun Siu ,  Nitika Mathur ,  Gioacchino Tangari ,  Aashna Devang Kanuga

IPC: G06N20/00 ,  G06F40/58 ,  G06F40/284 ,  G06F40/237

CPC classification number: G06N20/00 ,  G06F40/58 ,  G06F40/284 ,  G06F40/237 ,  G06F40/35

Abstract: Techniques are disclosed herein for synthesizing synthetic training data to facilitate training a natural language to logical form model. In one aspect, training data can be synthesized from original under a framework based on templates and a synchronous context-free grammar. In one aspect, training data can be synthesized under a framework based on a probabilistic context-free grammar and a translator. In one aspect, training data can be synthesized under a framework based on tree-to-string translation. In one aspect, the synthetic training data can be combined with original training data in order to train a machine learning model to translate an utterance to a logical form.

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：US20250095804A1

公开(公告)日：2025-03-20

申请号：US18830934

申请日：2024-09-11

Applicant: Oracle International Corporation

Inventor： Syed Najam Abbas Zaidi ,  Shiquan Yang ,  Poorya Zaremoodi ,  Nitika Mathur ,  Shubham Pawankumar Shah ,  Arash Shamaei ,  Sagar Kalyan Gollamudi

IPC: G16H10/60 ,  G06F40/295

Abstract: Techniques are disclosed for automatically generating Subjective, Objective, Assessment and Plan (SOAP) notes. Particularly, techniques are disclosed for automatic SOAP note generation using task decomposition. A text transcript is accessed and segmented into portions. The text transcript can correspond to an interaction between a first entity and a second entity. Machine-learning model prompts are used to extract entities and facts for the respective portions and generate SOAP note sections based at least in-part on the facts. A SOAP note is generated by combining the SOAP note sections. The SOAP note can be stored in a database in association with at least one of the first entity and the second entity.

公开(公告)号：US20250095803A1

公开(公告)日：2025-03-20

申请号：US18829834

申请日：2024-09-10

Applicant: Oracle International Corporation

Inventor： Syed Najam Abbas Zaidi ,  Shiquan Yang ,  Poorya Zaremoodi ,  Nitika Mathur ,  Shubham Pawankumar Shah ,  Arash Shamaei ,  Sagar Kalyan Gollamudi

IPC: G16H10/60 ,  G06F40/205 ,  G06F40/295

Abstract: Techniques are disclosed for automatically generating Subjective, Objective, Assessment and Plan (SOAP) notes. Particularly, techniques are disclosed for identifying entities for automatic SOAP note generation. A text transcript is accessed and segmented into portions. The text transcript can correspond to an interaction between a first entity and a second entity. One or more entities for the respective portions are identified using one or more machine-learning models. Facts are from the respective portions using the one or more machine-learning models based at least in-part on the context of the respective portions. A SOAP note is generated using the one or more machine-learning models and based at least in-part on the facts. The SOAP note can be stored in a database in association with at least one of the first entity and the second entity.

公开(公告)号：US20240061833A1

公开(公告)日：2024-02-22

申请号：US18218385

申请日：2023-07-05

Applicant: Oracle International Corporation

Inventor： Gioacchino Tangari ,  Nitika Mathur ,  Philip Arthur ,  Cong Duy Vu Hoang ,  Aashna Devang Kanuga ,  Steve Wai-Chun Siu ,  Syed Najam Abbas Zaidi ,  Poorya Zaremoodi ,  Thanh Long Duong ,  Mark Edward Johnson

IPC: G06F16/2452 ,  G06F16/242 ,  G06F40/247 ,  G06F40/284

CPC classification number: G06F16/24522 ,  G06F16/243 ,  G06F40/247 ,  G06F40/284

Abstract: Techniques are disclosed for augmenting training data for training a machine learning model to generate database queries. Training data comprising a first training example comprising a first natural language utterance, a logical form for the first natural language utterance, and associated first metadata is obtained. From the first training example, a template utterance is generated. A second natural language utterance is generated by filling slots in the template utterance based on a database schema and database values. Updated metadata is produced based on the first metadata and the second natural language utterance. A second training example is generated, comprising the second natural language utterance, the logical form for the first natural language utterance, and the updated metadata. The training data is augmented by adding the second training example. A machine learning model is trained to generate a database query comprising the database operation using the augmented training data set.

公开(公告)号：US20230186025A1

公开(公告)日：2023-06-15

申请号：US18065387

申请日：2022-12-13

Applicant: Oracle International Corporation

Inventor： Jae Min John ,  Vishal Vishnoi ,  Mark Edward Johnson ,  Thanh Long Duong ,  Srinivasa Phani Kumar Gadde ,  Balakota Srinivas Vinnakota ,  Shivashankar Subramanian ,  Cong Duy Vu Hoang ,  Yakupitiyage Don Thanuja Samodhye Dharmasiri ,  Nitika Mathur ,  Aashna Devang Kanuga ,  Philip Arthur ,  Gioacchino Tangari ,  Steve Wai-Chun Siu

IPC: G06F40/284 ,  G06F40/295 ,  G06F40/42

CPC classification number: G06F40/284 ,  G06F40/295 ,  G06F40/42

Abstract: Techniques for preprocessing data assets to be used in a natural language to logical form model based on scalable search and content-based schema linking. In one particular aspect, a method includes accessing an utterance, classifying named entities within the utterance into predefined classes, searching value lists within the database schema using tokens from the utterance to identify and output value matches including: (i) any value within the value lists that matches a token from the utterance and (ii) any attribute associated with a matching value, generating a data structure by organizing and storing: (i) each of the named entities and an assigned class for each of the named entities, (ii) each of the value matches and the token matching each of the value matches, and (iii) the utterance, in a predefined format for the data structure, and outputting the data structure.

公开(公告)号：US20250095807A1

公开(公告)日：2025-03-20

申请号：US18883782

申请日：2024-09-12

Applicant: Oracle International Corporation

Inventor： Syed Najam Abbas Zaidi ,  Poorya Zaremoodi ,  Shiquan Yang ,  Nitika Mathur ,  Shubham Pawankumar Shah ,  Arash Shamaei ,  Sagar Kalyan Gollamudi

IPC: G16H10/60 ,  G10L15/26

Abstract: Techniques are disclosed for automatically generating prompts. A method comprises accessing first prompts, wherein each of the first prompts is a prompt for generating a portion of a SOAP note using a machine-learning model. For each respective first prompt of the first prompts: (i) using the respective first prompt to obtain a first result from a first machine-learning model, (ii) using the respective first prompt and the first result to obtain a second result from a second machine-learning model, the second result including an assessment of the first result, (iii) using the second result to obtain a third result from a third machine-learning model, the third result including a second prompt, (iv) setting the second prompt as the respective first prompt, (v) repeating steps (i)-(iv) a number of times to obtain a production prompt, (vi) adding the production prompt to a collection of prompts; and storing the collection of prompts.

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.