公开(公告)号：US20250095638A1

公开(公告)日：2025-03-20

申请号：US18891686

申请日：2024-09-20

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Jaejin CHO ,  Rakshith Sharma Srinivasa ,  Chou-chang Yang ,  Yashas Malur Saidutta ,  Ching-Hua Lee ,  Yilin Shen ,  Hongxia Jin

IPC: G10L15/06 ,  G10L15/18 ,  G10L15/183

Abstract: A method includes: receiving one or more training text sentences; generating one or more training vectors based on inputting the one or more training sentences input into a text encoder, the one or more training vectors corresponding to one or more operations that an electronic device is configured to perform; generating one or more speech vectors based on one or more speech utterances input into a speech encoder; generating a similarity matrix that compares each of the one or more training vectors with each of the one or more speech vectors; and updating at least one of the text encoder and the speech encoder based on the similarity matrix.

公开(公告)号：US12210835B2

公开(公告)日：2025-01-28

申请号：US17946400

申请日：2022-09-16

Applicant: Samsung Electronics Co., Ltd.

Inventor： Peixi Xiong ,  Yilin Shen ,  Hongxia Jin

IPC: G06F40/30 ,  G06F40/284 ,  G06F40/289 ,  G06V10/40 ,  G06V10/70 ,  G06V10/80 ,  G06F16/33 ,  G06V10/426 ,  G06V10/762 ,  G06V10/764 ,  G06V10/82

Abstract: In one embodiment, a method includes accessing an image and a natural-language question regarding the image and extracting, from the image, a first set of image features at a first level of granularity and a second set of image features at a second level of granularity. The method further includes extracting, from the question, a first set of text features at the first level of granularity and a second set of text features at the second level of granularity; generating a first output representing an alignment between the first set of image features and the first set of text features; generating a second output representing an alignment between the second set of image features and the second set of text features; and determining an answer to the question based on the first output and the second output.

公开(公告)号：US20240386575A1

公开(公告)日：2024-11-21

申请号：US18526875

申请日：2023-12-01

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Jing Zhu ,  Karim Ahmed ,  Wenbo Li ,  Yilin Shen ,  Hongxia Jin

IPC: G06T7/194 ,  G06T3/40

Abstract: Provided is a method and apparatus for obtaining a foreground image from an input image containing the foreground object in a scene. Embodiments use multi-scale convolutional attention values, one or more hamburger heads and one or more multilayer perceptrons to obtain a segmentation map of the input image. In some embodiments, progressive segmentation is applied to obtain the segmentation map.

公开(公告)号：US20240339123A1

公开(公告)日：2024-10-10

申请号：US18470788

申请日：2023-09-20

Applicant: Samsung Electronics Co., Ltd.

Inventor： Chou-Chang Yang ,  Yashas Malur Saidutta ,  Rakshith Sharma Srinivasa ,  Ching-Hua Lee ,  Yilin Shen ,  Hongxia Jin

IPC: G10L21/0232 ,  G10L15/06 ,  G10L15/08 ,  G10L25/18

CPC classification number: G10L21/0232 ,  G10L15/063 ,  G10L15/08 ,  G10L25/18 ,  G10L2015/088

Abstract: A method includes receiving an audio input and generating a noisy time-frequency representation based on the audio input. The method also includes providing the noisy time-frequency representation to a noise management model trained to predict a denoising mask and a signal presence probability (SPP) map indicating a likelihood of a presence of speech. The method further includes determining an enhanced spectrogram using the denoising mask and the noisy time-frequency representation. The method also includes providing the enhanced spectrogram and the SPP map as inputs to a keyword classification model trained to determine a likelihood of a keyword being present in the audio input. In addition, the method includes, responsive to determining that a keyword is in the audio input, transmitting the audio input to a downstream application associated with the keyword.

公开(公告)号：US20240203143A1

公开(公告)日：2024-06-20

申请号：US18454459

申请日：2023-08-23

Applicant: Samsung Electronics Co., Ltd.

Inventor： Lingyu Zhang ,  Ting Hua ,  Yilin Shen ,  Hongxia Jin

IPC: G06V20/70 ,  G06F40/284 ,  G06V10/774

CPC classification number: G06V20/70 ,  G06F40/284 ,  G06V10/774

Abstract: A method includes obtaining an image, a set of attribute labels, and a set of object labels and performing prompt tuning of a pre-trained vision-language model having first and second textual encoders and a vision encoder. The model is trained during prompt tuning to select one attribute label and one object label that match content in the image. Performing the prompt tuning includes, for each attribute label-object label pair, generating object textual features associated with the object label using the first textual encoder, generating attribute textual features associated with the attribute label using the second textual encoder, and generating image features associated with the image using the vision encoder. Intermediate outputs from initial layers of the textual encoders and the vision encoder are combined to generate layer-specific learnable prompt tokens that are appended to inputs of specified layers in the first and second textual encoders and the vision encoder.

公开(公告)号：US20240185850A1

公开(公告)日：2024-06-06

申请号：US18352601

申请日：2023-07-14

Applicant: Samsung Electronics Co., Ltd.

Inventor： Rakshith Sharma Srinivasa ,  Yashas Malur Saidutta ,  Ching-Hua Lee ,  Chou-Chang Yang ,  Yilin Shen ,  Hongxia Jin

IPC: G10L15/22 ,  G10L15/02 ,  G10L15/06 ,  G10L15/18 ,  G10L25/78

CPC classification number: G10L15/22 ,  G10L15/02 ,  G10L15/063 ,  G10L15/18 ,  G10L25/78 ,  G10L2015/088 ,  G10L2015/223

Abstract: A method includes extracting, using a keyword detection model, audio features from audio data. The method also includes processing the audio features by a first layer of the keyword detection model configured to predict a first likelihood that the audio data includes speech. The method also includes processing the audio features by a second layer of the keyword detection model configured to predict a second likelihood that the audio data includes keyword-like speech. The method also includes processing the audio features by a third layer of the keyword detection model configured to predict a third likelihood, for each of a plurality of possible keywords, that the audio data includes the keyword. The method also includes identifying a keyword included in the audio data. The method also includes generating instructions to perform an action based at least in part on the identified keyword.

公开(公告)号：US11972327B2

公开(公告)日：2024-04-30

申请号：US15967327

申请日：2018-04-30

Applicant: Samsung Electronics Co., Ltd.

Inventor： Vijay Srinivasan ,  Christian Koehler ,  Hongxia Jin

IPC: G06N20/00 ,  G06N7/01

CPC classification number: G06N20/00 ,  G06N7/01

Abstract: A method for action automation includes determining, using an electronic device, an action based on domain information. Activity patterns associated with the action are retrieved. For each activity pattern, a candidate action rule is determined. Each candidate action rule specifies one or more pre-conditions when the action occurs. One or more preferred candidate action rules are determined from multiple candidate action rules for automation of the action.

公开(公告)号：US20240104309A1

公开(公告)日：2024-03-28

申请号：US18465648

申请日：2023-09-12

Applicant: Samsung Electronics Co., Ltd.

Inventor： Yen-Chang Hsu ,  Harshavardhan Kamarthi ,  Yilin Shen ,  Hongxia Jin

IPC: G06F40/35 ,  G06F40/166 ,  G06F40/284 ,  G06F40/40 ,  G06N3/09

CPC classification number: G06F40/35 ,  G06F40/166 ,  G06F40/284 ,  G06F40/40 ,  G06N3/09

Abstract: A method includes receiving an input for a large language model (LLM) from a user. The method also includes generating one or more token embeddings based on the input. The method further includes generating one or more prompt embeddings based on the input using a contextual prompt generator (CPG), the one or more prompt embeddings representing new or updated information that is not contained in existing knowledge of the LLM. The method also includes providing the one or more token embeddings and the one or more prompt embeddings to the LLM. In addition, the method includes outputting a prediction based on the one or more token embeddings and the one or more prompt embeddings using the LLM, wherein the prediction reflects the new or updated information represented by the one or more prompt embeddings.

公开(公告)号：US20240080423A1

公开(公告)日：2024-03-07

申请号：US18057126

申请日：2022-11-18

Applicant: Samsung Electronics Co., Ltd.

Inventor： Wenbo Li ,  Zhipeng Mo ,  Yi Wei ,  Burak Uzkent ,  Qian Lou ,  Yilin Shen ,  Hongxia Jin

IPC: H04N9/64

CPC classification number: H04N9/64

Abstract: A method includes obtaining raw image data, where the raw image data includes data values each having most significant bits and least significant bits. The method also includes providing the raw image data to a trained machine learning model and generating processed image data using the trained machine learning model. The method further includes presenting an image based on the processed image data. The trained machine learning model is trained to modulate a feature map associated with the most significant bits of the data values of the raw image data based on the least significant bits of the data values of the raw image data in order to generate a fusion of the most significant bits and the least significant bits of the data values of the raw image data.

公开(公告)号：US11775815B2

公开(公告)日：2023-10-03

申请号：US16535380

申请日：2019-08-08

Applicant: Samsung Electronics Co., Ltd.

Inventor： Yilin Shen ,  Yue Deng ,  Avik Ray ,  Hongxia Jin

IPC: G06N3/08 ,  G06N5/04

CPC classification number: G06N3/08 ,  G06N5/04

Abstract: An electronic device including a deep memory model includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to receive input data to the deep memory model. The at least one processor is also configured to extract a history state of an external memory coupled to the deep memory model based on the input data. The at least one processor is further configured to update the history state of the external memory based on the input data. In addition, the at least one processor is configured to output a prediction based on the extracted history state of the external memory.