公开(公告)号：US20240153297A1

公开(公告)日：2024-05-09

申请号：US18501982

申请日：2023-11-03

Applicant: Google LLC

Inventor： Zizhao Zhang ,  Zifeng Wang ,  Vincent Perot ,  Jacob Devlin ,  Chen-Yu Lee ,  Guolong Su ,  Hao Zhang ,  Tomas Jon Pfister

IPC: G06V30/24 ,  G06F16/21 ,  G06V30/19 ,  G06V30/412

CPC classification number: G06V30/24 ,  G06F16/211 ,  G06V30/19147 ,  G06V30/412

Abstract: A method for extracting entities comprises obtaining a document that includes a series of textual fields that includes a plurality of entities. Each entity represents information associated with a predefined category. The method includes generating, using the document, a series of tokens representing the series of textual fields. The method includes generating an entity prompt that includes the series of tokens and one of the plurality of entities and generating a schema prompt that includes a schema associated with the document. The method includes generating a model query that includes the entity prompt and the schema prompt and determining, using an entity extraction model and the model query, a location of the one of the plurality of entities among the series of tokens. The method includes extracting, from the document, the one of the plurality of entities using the location of the one of the plurality of entities.

公开(公告)号：US20250036886A1

公开(公告)日：2025-01-30

申请号：US18766812

申请日：2024-07-09

Applicant: Google LLC

Inventor： Chen-Yu Lee ,  Alexander Ratner ,  Tomas Pfister ,  Chun-Liang Li ,  Yasuhisa Fujii ,  Ranjay Krishna ,  Cheng-Yu Hsieh ,  Si-An Chen

IPC: G06F40/40 ,  G06N3/0475

Abstract: Using a large language model to comply with a user request. The large language model receives tool documentation for each of one or more tools, and analyzes the tool documentation for each of the one or more tools to determine, for each tool, one or more tasks that the tool is operable to perform. Upon receiving a request from a user, the large language model generates a plan for complying with the request by using one or more of the tools, the plan including performance of one or more of the tasks.

公开(公告)号：US20230153980A1

公开(公告)日：2023-05-18

申请号：US18054524

申请日：2022-11-10

Applicant: Google LLC

Inventor： Kihyuk Sohn ,  Jinsung Yoon ,  Chun-Liang Li ,  Tomas Jon Pfister ,  Chen-Yu Lee

IPC: G06T7/00 ,  G06V10/762

CPC classification number: G06T7/0004 ,  G06V10/7625 ,  G06V10/7635 ,  G06T2207/20081 ,  G06V10/764

Abstract: A computer-implemented method includes receiving an anomaly clustering request that requests data processing hardware to assign each image of a plurality of images into one of a plurality of groups. The method also includes obtaining a plurality of images. For each respective image, the method includes extracting a respective set of patch embeddings from the respective image, determining a distance between the respective set of patch embeddings and each other set of patch embeddings, and assigning the respective image into one of the plurality of groups using the distances between the respective set of patch embeddings and each other set of patch embeddings.

公开(公告)号：US20240354504A1

公开(公告)日：2024-10-24

申请号：US18684557

申请日：2021-08-25

Applicant: Google LLC

Inventor： Chen-Yu Lee ,  Chun-Liang Li ,  Timothy Dozat ,  Vincent Perot ,  Guolong Su ,  Nan Hua ,  Joshua Ainslie ,  Renshen Wang ,  Yasuhisa Fujii ,  Tomas Pfister

IPC: G06F40/284 ,  G06V30/10 ,  G06V30/416

CPC classification number: G06F40/284 ,  G06V30/10 ,  G06V30/416

Abstract: Systems and methods for providing a structure-aware sequence model that can interpret a document's text without first inferring the proper reading order of the document. In some examples, the model may use a graph convolutional network to generate contextualized “supertoken” embeddings for each token, which are then fed to a transformer that employs a sparse attention paradigm in which attention weights for at least some supertokens are modified based on differences between predicted and actual values of the order and distance between the attender and attendee supertokens.

公开(公告)号：US20230274143A1

公开(公告)日：2023-08-31

申请号：US18173985

申请日：2023-02-24

Applicant: Google LLC

Inventor： Zizhao Zhang ,  Zifeng Wang ,  Chen-Yu Lee ,  Ruoxi Sun ,  Sayna Ebrahimi ,  Xiaoqi Ren ,  Guolong Su ,  Vincent Perot ,  Tomas Pfister ,  Han Zhang

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: A method for rehearsal-free continual learning includes obtaining a set of training samples where training sample in the set of training samples is associated with a respective task of a plurality of different tasks. The method includes obtaining a task-invariant prompt representative of learned knowledge common to each respective task of the plurality of different tasks. The method includes, for each respective task of the plurality of different tasks, obtaining a respective task-specific prompt representative of learned knowledge specific to the respective task. The method includes, during each of one or more training iterations, for each respective training sample in the set of training samples, selecting the respective task-specific prompt representative of the respective task of the respective training sample and training a model using the task-invariant prompt and the selected respective task-specific prompt.