公开(公告)号：US20150309180A1

公开(公告)日：2015-10-29

申请号：US14574194

申请日：2014-12-17

Applicant: Samsung Electronics Co., Ltd.

Inventor： Yifei JIANG ,  Jun YANG ,  Vijay SRINIVASAN ,  Shalinder S. SIDHU ,  Danny R. Bennett

IPC: G01S19/13 ,  G01C21/20

CPC classification number: G01C21/206 ,  G01S5/0252

Abstract: In one aspect, information of multiple anchor points is received and stored. The information of each anchor point includes Global Positioning System (GPS) data of a particular location and radio frequency (RF) data that was obtained at a device at the particular location. A geo coordinate is determined for an indoor location based on the RF data obtained at the indoor location and the information of the anchor points. Various embodiments pertain to software, systems, devices and methods relating to anchor points and/or the obtaining of a geo coordinate for a location.

Abstract translation: 一方面，接收并存储多个锚点的信息。 每个锚点的信息包括在特定位置处的设备处获得的特定位置和射频（RF）数据的全球定位系统（GPS）数据。 基于在室内获取的RF数据和锚点的信息，确定室内位置的地理坐标。 各种实施例涉及与锚定点相关的软件，系统，设备和方法和/或获得位置的地理坐标。

公开(公告)号：US20230252982A1

公开(公告)日：2023-08-10

申请号：US18077826

申请日：2022-12-08

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Dalkandura Arachchige K.S.S. GUNARATNA ,  Vijay SRINIVASAN ,  Hongxia JIN

IPC: G10L15/18 ,  G10L15/16

CPC classification number: G10L15/1822 ,  G10L15/16

Abstract: In an artificial intelligence model (AI model), input data is processed to provide both classification of the input data and a visualization of the process of the AI model. This is done by performing intent and slot classification of the input data, generating weights and binary classifier logits, performing feature fusion and classification. A graphical explanation is then output as a visualization along with logits.

公开(公告)号：US20250037710A1

公开(公告)日：2025-01-30

申请号：US18781617

申请日：2024-07-23

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Vikas YADAV ,  Zheng TANG ,  Vijay SRINIVASAN ,  Hongxia JIN

IPC: G10L15/18

Abstract: A method of interpreting a verbal input, may include: assigning a meaning classification to the verbal input, and a confidence score to the meaning classification; and based on the confidence score corresponding to the meaning classification of the verbal input being less than or equal to a threshold, generating at least one paraphrase of the verbal input using at least one large language model (LLM); assigning the meaning classification to the at least one paraphrase, and the confidence score to the meaning classification; and concatenating the verbal input, the at least one paraphrase, the meaning classification, and the confidence score to generate a concatenated input; inputting the concatenated input into the at least one LLM.