公开(公告)号：US20240403693A1

公开(公告)日：2024-12-05

申请号：US18203568

申请日：2023-05-30

Applicant: Oracle International Corporation

Inventor： Uday Bhaskar Yalamanchi ,  FNU Akshat ,  Prashanth Ramanathan ,  Abhiram Jagarlapudi ,  Ye Zhang ,  Aditya Banerjee ,  Varaprasad Ballingam ,  Athinder Patlola ,  Beiwen Guo ,  Varun Ketanbhai Shah ,  Safia Rahmat ,  Shreyas Vinayakumar ,  Jigar Mody ,  Elad Ziklik ,  Senthilkumar Ponnappan ,  Pranav Varia ,  Denesh Krishnan Rajaram ,  Hariharan Balasubramanian

IPC: G06N20/00

Abstract: Techniques for providing machine-learned (ML)-based artificial intelligence (AI) capabilities are described. In one technique, multiple AI capabilities are stored in a cloud environment. While the AI capabilities are stored, a request for a particular AI capability is received from a computing device of a user. Also, in response to receiving training data based on input from the user, the training data is stored in a tenancy, associated with the user, in the cloud environment. In response to receiving the request, the particular AI capability is accessed, a ML model is trained based on the particular AI capability and the training data to produce a trained ML model, and an endpoint, in the cloud environment, is generated that is associated with the trained ML model. The endpoint is provided to the tenancy associated with the user.

公开(公告)号：US20250022595A1

公开(公告)日：2025-01-16

申请号：US18222392

申请日：2023-07-14

Applicant: Oracle International Corporation

Inventor： Amir Hossein Rezaeian ,  Pranav Varia

IPC: G16H50/20 ,  G06F40/40 ,  G16H10/60 ,  G16H20/10 ,  G16H50/30

Abstract: Techniques for selecting medical items for presentation using an artificial intelligence architecture are provided. In one technique, summary note data that is composed by a physician for a patient is received. A machine-learned (ML) language model generates, based on the summary note data, a set of feature values. A profile of the patient and a profile of the physician are identified. An ML recommendation model determines, based on the profile of the patient, the profile of the physician, and the set of feature values, a plurality of candidate medical items. An ML reinforcement learning model generates a ranking of the plurality of candidate medical items. A subset of the plurality of candidate medical items is caused to be presented on a screen of a computing device based on the ranking.