公开(公告)号：US12298985B2

公开(公告)日：2025-05-13

申请号：US18344509

申请日：2023-06-29

Applicant: Google LLC

Inventor： Matthew Sharifi ,  Abhanshu Sharma ,  David Petrou

IPC: G06F16/2457 ,  G06F16/2452 ,  G06F16/58 ,  G06F16/583 ,  G06F16/903

Abstract: Methods, systems, and apparatus for receiving a query image, receiving one or more entities that are associated with the query image, identifying, for one or more of the entities, one or more candidate search queries that are pre-associated with the one or more entities, generating a respective relevance score for each of the candidate search queries, selecting, as a representative search query for the query image, a particular candidate search query based at least on the generated respective relevance scores and providing the representative search query for output in response to receiving the query image.

公开(公告)号：US12298822B2

公开(公告)日：2025-05-13

申请号：US18407949

申请日：2024-01-09

Applicant: Google LLC

Inventor： Suk Wong ,  Umakaran Nemallan ,  Ryan Schiller

IPC: G06F1/18 ,  H05K13/04

Abstract: The technology relates to a memory insertion apparatus for pushing memory modules into corresponding memory sockets on a circuit board. The memory insertion apparatus may include a frame and an actuation assembly coupled to the frame, and one or more cam assemblies each rotatably coupled to the frame and operatively coupled to the actuation assembly. Each cam assembly may include a central shaft extending in a longitudinal direction, and a plurality of cams each having a tip configured to engage one of the memory modules, the tip extending from the central shaft in a respective radial direction perpendicular to the longitudinal direction. A center of each cam may be offset from centers of adjacent ones of the cams by a pitch distance that is about equal to a pitch distance between centers of adjacent ones of the memory sockets.

公开(公告)号：US20250150641A1

公开(公告)日：2025-05-08

申请号：US18836986

申请日：2022-12-29

Applicant: GOOGLE LLC

Inventor： Jingning Han ,  Yaowu Xu ,  Joseph Young ,  In Suk Chong ,  Debargha Mukherjee

IPC: H04N19/91 ,  H04N19/18 ,  H04N19/70

Abstract: Entropy coding a sequence of syntax elements is described where an observation for a syntax element of the sequence is determined, and the observation is arithmetic coded using the probability model. Thereafter, the probability model is updated using a time-variant update rate to produce an updated probability model. Updating the probability model includes regularizing one or more probability values of the probability model so no probability of the updated probability model is below a defined minimum resolution. As a result, the use of a minimum probability value during the arithmetic coding, which can distort probability model, may be omitted.

公开(公告)号：US20250150260A1

公开(公告)日：2025-05-08

申请号：US19011963

申请日：2025-01-07

Applicant: Google LLC

Inventor： Eli Simon Fox-Epstein ,  Craig William Wright ,  Kevin Wei Li Yeo ,  Mariana Raykova ,  Karn Seth

IPC: H04L9/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium for retrieving information from a server. Methods can include a server receiving a set of client-encrypted queries. The server identifies a set of server-encrypted decryption keys and transmits the set to the client device. The server receives a set of client-server-encrypted decryption keys that includes the set of server-encrypted decryption keys encrypted by the client device. The server also receives a set of client-encrypted/client-derived decryption keys that were derived by the client device. The server generates matching a map that specifies matches between the set of client-server-encrypted decryption keys and the set of client-encrypted/client-derived decryption keys. The server filters the set of client-encrypted queries using the map to create a set of filtered client-encrypted queries and generates a set of query results.

公开(公告)号：US20250149171A1

公开(公告)日：2025-05-08

申请号：US18834165

申请日：2023-01-30

Applicant: Google LLC

Inventor： Jim Huibrecht Winkens ,  Alan Prasana Karthikesalingam ,  Krishnamurthy Dvijotham ,  Ali Taylan Cemgil ,  Sumedh Kedar Ghaisas

IPC: G16H50/20 ,  G06V10/764 ,  G16H30/20

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying data points using a deferral model that determines whether to classify the data point using an output of one or more diagnostic machine learning models or to defer the data point for classification by one or more users.

公开(公告)号：US20250149026A1

公开(公告)日：2025-05-08

申请号：US18915058

申请日：2024-10-14

Applicant: Google LLC

Inventor： Lun Wang ,  Om Dipakbhai Thakkar ,  Rajiv Mathews

IPC: G10L15/065 ,  G10L13/02

Abstract: A method includes obtaining an automatic speech recognition (ASR) model pre-trained on an initial training dataset, creating a set of canary speech utterances, and speeding up each canary speech utterance in the set of canary speech utterances. The operations also include fine-tuning the ASR model on the set of sped-up canary speech utterances and measuring un-intended memorization of the fine-tuned ASR model based on speech recognition results performed by the fine-tuned ASR model on the sped-up canary speech utterances.

公开(公告)号：US20250148713A1

公开(公告)日：2025-05-08

申请号：US18500700

申请日：2023-11-02

Applicant: Google LLC

Inventor： Kathryn Heal ,  John Patrick Flynn ,  Ryan Styles Overbeck ,  Michael Joseph Broxton ,  Stephen Anthony Lombardi ,  Clément Louis Jean-Claude Godard

IPC: G06T17/20 ,  G06T15/08

Abstract: A non-transitory computer-readable storage medium comprises instructions stored thereon. When executed by at least one processor, the instructions are configured to cause a computing system to at least generate multiple mesh cells based on multiple mesh layers, the multiple mesh layers representing a volumetric scene, the multiple mesh cells including multiple mesh blocks; select, from the multiple mesh blocks, k selected blocks based on densities of the multiple mesh blocks, k being a predetermined number; and store the selected blocks and identifiers of locations of the selected blocks.

公开(公告)号：US20250148365A1

公开(公告)日：2025-05-08

申请号：US18835869

申请日：2022-02-03

Applicant: Google LLC

Inventor： Dirk Ryan Padfield ,  Matthew Sharifi

IPC: G06N20/00

Abstract: Provided are systems and methods for continuous training of machine learning (ML) models on changing data. In particular, the present disclosure provides example approaches to model training that take advantage of constantly evolving data that may be available in various ancillary systems that contain large amounts of data, but which are not specific to or dedicated for model training.

公开(公告)号：US20250148343A1

公开(公告)日：2025-05-08

申请号：US18352555

申请日：2023-07-14

Applicant: Google LLC

Inventor： Benjamin Villalonga Correa ,  Sergio Boixo Castrillo ,  Michael Gabriel Newman

IPC: G06N10/70 ,  G06N10/40

Abstract: An enhanced matrix product state-based decoder is generated and employed to almost optimally detect and correct errors within a quantum computing and information processing system. The decoder takes as input a detector level error model that describes physical error channels and a set of error detections. This error model is improved using experimental data.

公开(公告)号：US20250148224A1

公开(公告)日：2025-05-08

申请号：US19015153

申请日：2025-01-09

Applicant: Google LLC

Inventor： Xavier Eduardo Garcia ,  Orhan Firat ,  Noah Constant ,  Xiaoyue Guo ,  Parker Riley

IPC: G06F40/58 ,  G06F40/166 ,  G06F40/197 ,  G06F40/253 ,  G06F40/56 ,  G06N3/045 ,  G06N3/047 ,  G06N3/08 ,  G06N3/084

Abstract: The technology provides a model-based approach for multilingual text rewriting that is applicable across many languages and across different styles including formality levels or other textual attributes. The model is configured to manipulate both language and textual attributes jointly. This approach supports zero-shot formality-sensitive translation, with no labeled data in the target language. An encoder-decoder architectural approach with attribute extraction is used to train rewriter models that can thus be used in “universal” textual rewriting across many different languages. A cross-lingual learning signal can be incorporated into the training approach. Certain training processes do not employ any exemplars. This approach enables not just straight translation, but also the ability to create new sentences with different attributes.