公开(公告)号：US20250118401A1

公开(公告)日：2025-04-10

申请号：US17143083

申请日：2021-01-06

Applicant: Google LLC

Inventor： Edward Choi ,  Andrew M. Dai ,  Gerardo Flores ,  Yuan Xue ,  Michael Ward Dusenberry ,  Zhen Xu ,  Yujia Li

IPC: G16H15/00 ,  G06N3/08 ,  G16H50/20

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing data about a medical encounter using neural networks. One of the methods includes obtaining features for a medical encounter associated with the patient, each feature representing a corresponding health event associated with the medical encounter and each of the plurality of features belonging to a vocabulary of possible features that each represent a different health event; and generating respective final embeddings for each of the features for the medical encounter by applying a sequence of one or more self-attention blocks to the features for the medical encounter, wherein each of the one or more self-attention blocks receives a respective block input for each of the features and applies self-attention over the block inputs to generate a respective block output for each of the features.

公开(公告)号：US20240282131A1

公开(公告)日：2024-08-22

申请号：US18421672

申请日：2024-01-24

Applicant: Google LLC

Inventor： Jie Ren ,  Zhe Liu ,  James Urquhart Allingham ,  Michael Ward Dusenberry ,  Dustin Tran ,  Yin Cui ,  Balaji Lakshminarayanan ,  Xiuye Gu

IPC: G06V20/70 ,  G06F40/40 ,  G06V10/74 ,  G06V10/764 ,  G06V10/776

CPC classification number: G06V20/70 ,  G06F40/40 ,  G06V10/761 ,  G06V10/764 ,  G06V10/776

Abstract: Systems and methods for zero-shot prompt ensembling for zero-shot classification with text-image models can include utilizing a pre-trained text-image model to perform downstream tasks based on prompt-based weighting. The systems and methods may adjust for frequency-based bias and may automatically determine different prompt associations with a given downstream task. The systems and methods can aggregate weighted text embeddings and then determine a classification output based on similarity measures between an image embedding and the aggregated weighted text embeddings.