公开(公告)号：US20230267919A1

公开(公告)日：2023-08-24

申请号：US18173699

申请日：2023-02-23

Applicant: TDK CORPORATION

Inventor： Rémi Louis Clément PONÇOT ,  Abbas ATAYA ,  Peter George HARTWELL

IPC: G10L15/08 ,  G10L15/30

CPC classification number: G10L15/08 ,  G10L15/30 ,  G10L2015/088

Abstract: In a method for human speech processing in an automatic speech recognition (ASR) system, human speech is received at a speech interface of the ASR system, wherein the ASR system comprises embedded componentry for onboard processing of the human speech and cloud-based componentry for remote processing of the human speech. A keyword is identified at the speech interface within a first portion of the human speech. Responsive to identifying the keyword, a second portion of the human speech is analyzed to identify at least one command, the second portion following the first portion. The at least one command is identified within the second portion of the human speech. The at least one command is selectively processed within at least one of the embedded componentry and the cloud-based componentry.

公开(公告)号：US20240201793A1

公开(公告)日：2024-06-20

申请号：US18542263

申请日：2023-12-15

Applicant: TDK CORPORATION

Inventor： Rémi Louis Clément PONÇOT ,  Juan S Mejia SANTAMARIA ,  Abbas ATAYA ,  Etienne De FORAS ,  Bruno FLAMENT

IPC: G06F3/01 ,  G01C21/16

CPC classification number: G06F3/017 ,  G01C21/16 ,  G06F3/011

Abstract: In a method for training a gesture recognition model, gesture data is collected from an inertial measurement unit (IMU) positioned on one side of a user, wherein the IMU is capable of collecting data when positioned on either side of the user. A transformation is applied to the gesture data, wherein the transformation generates transformed gesture data that is independent of either side of the user. A gesture recognition model is trained using the transformed gesture data.

公开(公告)号：US20230068190A1

公开(公告)日：2023-03-02

申请号：US17822692

申请日：2022-08-26

Applicant: TDK CORPORATION

Inventor： Abbas ATAYA

IPC: H04N19/149 ,  G08C17/02

Abstract: Described herein is a non-transitory computer readable storage medium having computer-readable program code stored thereon for causing a computer system to perform a method for processing data, the method comprising: receiving data, processing the data at a fixed code processing engine, wherein operation of the fixed code processing engine is controlled according to stored parameters, and classifying processed data at a fixed code classification engine, wherein operation of the fixed code classification engine is controlled according to the stored parameters.

公开(公告)号：US20230066206A1

公开(公告)日：2023-03-02

申请号：US17822709

申请日：2022-08-26

Applicant: TDK CORPORATION

Inventor： Abbas ATAYA ,  Mahdi HEYDARI

IPC: G06K9/62 ,  G06F9/50

Abstract: Disclosed herein is a method for designing a processing chain of a sensor system, the method comprising receiving a desired application comprising at least one activity for a sensor system to monitor, the sensor system comprising at least one sensor capable of generating sensor data based on sensing the at least one activity, accessing a database comprising a plurality of raw sensor data and a plurality of annotations corresponding to the plurality of raw sensor data, the plurality of annotations identifying activities corresponding to the plurality of raw sensor data, and automatically generating a processing chain of the sensor system for executing the desired application based on the desired application and the plurality of raw sensor data, the processing chain for processing the sensor data and for extracting at least one feature from the sensor data for use in sensing the at least one activity.

公开(公告)号：US20230063290A1

公开(公告)日：2023-03-02

申请号：US17822699

申请日：2022-08-26

Applicant: TDK CORPORATION

Inventor： Abbas ATAYA

IPC: G06K9/62 ,  G06N5/00

Abstract: Described herein is a method for modifying a trained classification model, the method comprising receiving feature data extracted from sensor data, classifying the feature data according to the trained classification model to identify a label corresponding to the feature data, wherein the trained classification model comprises a decision tree comprising a plurality of decision nodes for feature identification for a plurality of features, tracking identified features of the plurality of features over a predetermined amount of time, and responsive to determining that a feature of the trained classification model does not satisfy a frequency of usage threshold over the predetermined amount of time, deactivating a decision node of the decision tree of the trained classification model corresponding to the feature such that a subsequent instance of the classifying does not consider a deactivated decision nodes for subsequently received feature data.