公开(公告)号：US11741191B1

公开(公告)日：2023-08-29

申请号：US17939492

申请日：2022-09-07

Applicant: Google LLC

Inventor： Lukas Zilka

IPC: G06N20/00 ,  G06F18/214 ,  G06V10/46 ,  G06F17/18 ,  G06N3/02 ,  G06F18/2415

CPC classification number: G06F18/2148 ,  G06F17/18 ,  G06F18/2415 ,  G06N3/02 ,  G06N20/00 ,  G06V10/46 ,  G06V10/473

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for collaboratively training an interaction prediction machine learning model using a plurality of user devices in a manner that respects user privacy. In one aspect, the machine learning model is configured to process an input comprising: (i) a search query, and (ii) a data element, to generate an output which characterizes a likelihood that a given user would interact with the data element if the data element were presented to the given user on a webpage identified by a search result responsive to the search query.

公开(公告)号：US12147500B2

公开(公告)日：2024-11-19

申请号：US18350860

申请日：2023-07-12

Applicant: GOOGLE LLC

Inventor： Lukas Zilka

IPC: G06F18/214 ,  G06F17/18 ,  G06F18/2415 ,  G06N3/02 ,  G06N20/00 ,  G06V10/46

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for collaboratively training an interaction prediction machine learning model using a plurality of user devices in a manner that respects user privacy. In one aspect, the machine learning model is configured to process an input comprising: (i) a search query, and (ii) a data element, to generate an output which characterizes a likelihood that a given user would interact with the data element if the data element were presented to the given user on a webpage identified by a search result responsive to the search query.

公开(公告)号：US20250077618A1

公开(公告)日：2025-03-06

申请号：US18949575

申请日：2024-11-15

Applicant: GOOGLE LLC

Inventor： Lukas Zilka

IPC: G06F18/214 ,  G06F17/18 ,  G06F18/2415 ,  G06N3/02 ,  G06N20/00 ,  G06V10/46

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for collaboratively training an interaction prediction machine learning model using a plurality of user devices in a manner that respects user privacy. In one aspect, the machine learning model is configured to process an input comprising: (i) a search query, and (ii) a data element, to generate an output which characterizes a likelihood that a given user would interact with the data element if the data element were presented to the given user on a webpage identified by a search result responsive to the search query.

公开(公告)号：US20250028744A1

公开(公告)日：2025-01-23

申请号：US18714673

申请日：2022-01-07

Applicant: Google LLC

Inventor： Matthew Sharifi ,  Maryam Karimzadehgan ,  Lukas Zilka ,  Julian Odell ,  Jesper Andersen

IPC: G06F16/332 ,  G06F16/33 ,  G06F40/40 ,  G06N3/006 ,  G06N20/00

Abstract: Systems and methods for generating a domain-specific conversational automated assistant. In some examples, a conversational language model is used to generate a target answer and a target action recommendation in response to each of a set of in-domain training questions. In some examples, the conversational language model is further used to generate follow-up questions to one or more of its generated target answers, and to then generate a target answer and target action recommendation to each generated follow-up question. In some examples, the processing system also generates a set of out-of-domain training examples including an out-of-domain question, a predetermined target answer, and a predetermined target action recommendation. The automated assistant may then be trained to predict the generated target answers and target action recommendations based on the associated training question or generated follow-up question, as well as any prior questions and answers in the conversation.

公开(公告)号：US20230359818A1

公开(公告)日：2023-11-09

申请号：US18246326

申请日：2020-12-18

Applicant: Google LLC

Inventor： Matthew Sharifi ,  Sebastian Millius ,  Qi Wang ,  Yunpeng Li ,  Shankar Kumar ,  Lukas Zilka ,  Simon Tong ,  Martin Sundermeyer

IPC: G06F40/253

CPC classification number: G06F40/253

Abstract: A computing device may receive inputted text and perform, using one or more neural networks, on-device grammar checking of a sequence of words in the inputted text, including determining, using the one or more neural networks, a grammatically correct version of the sequence of words and determining that the sequence of words does not match the grammatically correct version of the sequence of words. The computing device may, in response to determining that the sequence of words does not match the grammatically correct version of the sequence of words, output, for display at a display device, at least a portion of the grammatically correct version of the sequence of words as a suggested replacement for at least a sequence of the sequence of words in the inputted text.

公开(公告)号：US20230350978A1

公开(公告)日：2023-11-02

申请号：US18350860

申请日：2023-07-12

Applicant: GOOGLE LLC

Inventor： Lukas Zilka

IPC: G06V10/46 ,  G06N20/00 ,  G06F17/18 ,  G06N3/02

CPC classification number: G06F18/2148 ,  G06F17/18 ,  G06F18/2415 ,  G06N3/02 ,  G06N20/00 ,  G06V10/46 ,  G06V10/473

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for collaboratively training an interaction prediction machine learning model using a plurality of user devices in a manner that respects user privacy. In one aspect, the machine learning model is configured to process an input comprising: (i) a search query, and (ii) a data element, to generate an output which characterizes a likelihood that a given user would interact with the data element if the data element were presented to the given user on a webpage identified by a search result responsive to the search query.