公开(公告)号：US20240361737A1

公开(公告)日：2024-10-31

申请号：US18604152

申请日：2024-03-13

Applicant: Apple Inc.

Inventor： Lukas M. Marti ,  Lili Cao ,  Michael P. Dal Santo

IPC: G05B15/02 ,  G06N7/01 ,  G06N20/00 ,  H04L12/28 ,  H04M1/72454 ,  H04W4/02 ,  H04W4/021 ,  H04W4/029

CPC classification number: G05B15/02 ,  G06N7/01 ,  G06N20/00 ,  H04L12/282 ,  H04L12/2827 ,  H04M1/72454 ,  H04W4/021 ,  H04W4/023 ,  H04W4/029 ,  H04L2012/2841

Abstract: A mobile device can provide predictive user assistance based on various sensor readings, independently of or in addition to a location of the mobile device. The mobile device can determine a context of an event. The mobile device can store the context and a label of the event on a storage device. The label can be provided automatically by the mobile device or by the external system without user input. At a later time, the mobile device can match new sensor readings with the stored context. If a match is found, the mobile device can predict that the user is about to perform the action or recognize that the user has performed the action again. The mobile device can perform various operations, including, for example, providing user assistance, based on the prediction or recognition.

公开(公告)号：US12130912B2

公开(公告)日：2024-10-29

申请号：US17515151

申请日：2021-10-29

Applicant: Oracle International Corporation

Inventor： François Gauthier ,  Sora Bae

IPC: G06F21/55 ,  G06N7/01 ,  G06N20/00

CPC classification number: G06F21/554 ,  G06N7/01 ,  G06N20/00 ,  G06F2221/034

Abstract: A method for detecting a deserialization attack may include identifying, in a byte stream, a class name corresponding to a class, generating, for the class, a feature vector, generating, by applying a benign deserialization model to the feature vector, a benign probability window, generating, by applying a malicious deserialization model to the feature vector, a malicious probability window, comparing the benign probability window and the malicious probability window to obtain a comparison result, and determining, based on the comparison result, that the class is malicious.

公开(公告)号：US12124979B2

公开(公告)日：2024-10-22

申请号：US18326851

申请日：2023-05-31

Applicant: SAUDI ARABIAN OIL COMPANY

Inventor： Ziyad Alsahlawi ,  Mahmoud Adnan Alqurashi ,  Ossama R. Sehsah

IPC: G06Q10/04 ,  G06F18/214 ,  G06K19/077 ,  G06N3/08 ,  G06N5/04 ,  G06N7/01 ,  G06N20/00 ,  G06Q10/0639 ,  G06V10/774 ,  G06V20/52

CPC classification number: G06Q10/04 ,  G06F18/214 ,  G06K19/07762 ,  G06N3/08 ,  G06N5/04 ,  G06N7/01 ,  G06N20/00 ,  G06Q10/0639 ,  G06V10/774 ,  G06V20/52

Abstract: A machine-learning ecosystem includes a correlation module for building at least one prediction model based on at least one data input including at least one input parameter and at least one output parameter, the prediction model relating the output parameter to the input parameter. The correlation module performs at least one threshold check on the prediction model to assess the robustness of the prediction model. The ecosystem further includes a decision module communicatively coupled to the correlation module and receiving the prediction model from the correlation module. Based on a verification check at the decision module, a confirmation, a deferral, or a rejection of the prediction model is sent from the decision module to the correlation module.

公开(公告)号：US12122053B2

公开(公告)日：2024-10-22

申请号：US16916017

申请日：2020-06-29

Applicant: NVIDIA Corporation

Inventor： Carolyn Linjon Chen ,  Yashraj Shyam Narang ,  Fabio Tozeto Ramos ,  Dieter Fox

IPC: B25J9/16 ,  B25J11/00 ,  G01N15/00 ,  G06F17/18 ,  G06F18/214 ,  G06F30/27 ,  G06N3/08 ,  G06N7/01 ,  G06T7/00 ,  G06T7/77 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56 ,  G06V20/64

CPC classification number: B25J9/1671 ,  B25J9/163 ,  B25J11/008 ,  G06F17/18 ,  G06F18/214 ,  G06F30/27 ,  G06N3/08 ,  G06N7/01 ,  G06T7/0004 ,  G06T7/77 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56 ,  G06V20/64 ,  G01N15/00 ,  G06T2207/10028

Abstract: Apparatuses, systems, and techniques to identify at least one physical characteristic of materials from computer simulations of manipulations of materials. In at least one embodiment, physical characteristics are determined by comparing measured statistics of observed manipulations to simulations of manipulations using a simulator trained with a likelihood-free inference engine.

公开(公告)号：US20240347071A1

公开(公告)日：2024-10-17

申请号：US18756814

申请日：2024-06-27

Applicant: T-Mobile USA, Inc.

Inventor： Yasmin Karimli ,  Ryan Cyrus Khamneian ,  Jie Hui ,  Antoine T. Tran

IPC: G10L25/51 ,  G06N7/01 ,  G06N20/00 ,  G10L15/14 ,  H04M3/22 ,  H04W4/029

CPC classification number: G10L25/51 ,  G06N7/01 ,  G06N20/00 ,  G10L15/14 ,  H04M3/2218 ,  H04W4/029

Abstract: Described herein are techniques, devices, and systems for training a machine learning model(s) and/or artificial intelligence algorithm(s) to determine where a mobile device (and, hence, a user of the mobile device) is located based on audio data associated with the mobile device and/or contextual data associated with the mobile device. The machine learning techniques may be used to determine contextual information about users, such as determining that a particular location is likely to be a user's home, office, or the like, based on movement patterns exhibited in the data associated with a user's mobile device. Once trained, the machine learning model(s) is usable to classify a mobile device as having been located at one of multiple candidate locations, such as indoors or outdoors, at a particular time. The described techniques can improve the accuracy of determining a mobile device's location, among other technical benefits.

公开(公告)号：US20240345566A1

公开(公告)日：2024-10-17

申请号：US18626984

申请日：2024-04-04

Applicant: R4N63R Capital LLC

Inventor： Prasad Narasimha AKELLA ,  Ananya Honnedevasthana ASHOK ,  Zakaria Ibrahim ASSOUL ,  Krishnendu CHAUDHURY ,  Sameer GUPTA ,  Sujay Venkata Krishna NARUMANCHI ,  David Scott PRAGER ,  Devashish SHANKAR ,  Ananth UGGIRALA ,  Yash Raj CHHABRA

IPC: G05B19/418 ,  B25J9/16 ,  G01M99/00 ,  G05B19/423 ,  G05B23/02 ,  G06F9/448 ,  G06F9/48 ,  G06F11/07 ,  G06F11/34 ,  G06F16/22 ,  G06F16/23 ,  G06F16/2455 ,  G06F16/901 ,  G06F16/9035 ,  G06F16/904 ,  G06F18/21 ,  G06F30/20 ,  G06F30/23 ,  G06F30/27 ,  G06F111/10 ,  G06F111/20 ,  G06N3/006 ,  G06N3/008 ,  G06N3/04 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G06N3/084 ,  G06N7/01 ,  G06N20/00 ,  G06Q10/06 ,  G06Q10/0631 ,  G06Q10/0639 ,  G06Q10/083 ,  G06Q50/26 ,  G06T19/00 ,  G06V10/25 ,  G06V10/44 ,  G06V10/82 ,  G06V20/52 ,  G06V40/20 ,  G09B19/00 ,  G16H10/60

CPC classification number: G05B19/4183 ,  G05B19/41835 ,  G06F9/4498 ,  G06F9/4881 ,  G06F11/0721 ,  G06F11/079 ,  G06F11/3452 ,  G06F16/2228 ,  G06F16/2365 ,  G06F16/24568 ,  G06F16/9024 ,  G06F16/9035 ,  G06F16/904 ,  G06F30/20 ,  G06F30/23 ,  G06F30/27 ,  G06N3/008 ,  G06N3/04 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G06N3/084 ,  G06N7/01 ,  G06N20/00 ,  G06Q10/06 ,  G06Q10/063112 ,  G06Q10/06316 ,  G06Q10/06393 ,  G06Q10/06395 ,  G06Q10/06398 ,  G06T19/006 ,  G06V10/25 ,  G06V10/454 ,  G06V10/82 ,  G06V20/52 ,  G06V40/20 ,  G09B19/00 ,  B25J9/1664 ,  B25J9/1697 ,  G01M99/005 ,  G05B19/41865 ,  G05B19/423 ,  G05B23/0224 ,  G05B2219/32056 ,  G05B2219/36442 ,  G06F18/217 ,  G06F2111/10 ,  G06F2111/20 ,  G06N3/006 ,  G06Q10/083 ,  G06Q50/26 ,  G16H10/60

Abstract: The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for automatic creation of certificates for each instance of a subject product or service. The certificate can string together snippets of the sensor streams along with indicators of cycles, processes, action, sequences, objects, parameters and the like captured in the sensor streams.

公开(公告)号：US12120141B2

公开(公告)日：2024-10-15

申请号：US17387741

申请日：2021-07-28

Applicant: Hewlett-Packard Development Company, L.P.

Inventor： Narendra Kumar Chincholikar ,  Sanket Anavkar ,  Vaibhav Tarange ,  Manohar Lal Kalwani

IPC: H04L29/06 ,  G06N5/04 ,  G06N7/00 ,  G06N7/01 ,  H04L9/40

CPC classification number: H04L63/1433 ,  G06N5/04 ,  G06N7/01

Abstract: In an example implementation according to aspects of the present disclosure, a system, method, and storage medium to identify and score security vulnerabilities is disclosed. A memory and a processor receive security-related data from a plurality of client computing devices, create a security score for each the client computing devices. The processor identifies a subset of the client computing devices with security scores surpassing a threshold and remediates a security vulnerability on each of the subset.

公开(公告)号：US12119088B2

公开(公告)日：2024-10-15

申请号：US17899539

申请日：2022-08-30

Applicant: ILLUMINA, INC.

Inventor： Kishore Jaganathan ,  Anindita Dutta ,  Dorna Kashefhaghighi ,  John Randall Gobbel ,  Amirali Kia

IPC: G06F16/907 ,  G06F18/21 ,  G06F18/214 ,  G06F18/23 ,  G06F18/23211 ,  G06F18/24 ,  G06F18/2415 ,  G06F18/2431 ,  G06N3/04 ,  G06N3/08 ,  G06N3/084 ,  G06N7/01 ,  G06V10/26 ,  G06V10/44 ,  G06V10/75 ,  G06V10/762 ,  G06V10/764 ,  G06V10/77 ,  G06V10/778 ,  G06V10/82 ,  G06V10/98 ,  G06V20/69 ,  G16B40/00 ,  G16B40/20 ,  G06N5/046 ,  G06V20/40

CPC classification number: G16B40/20 ,  G06F16/907 ,  G06F18/214 ,  G06F18/217 ,  G06F18/23 ,  G06F18/23211 ,  G06F18/24 ,  G06F18/2415 ,  G06F18/2431 ,  G06N3/04 ,  G06N3/08 ,  G06N3/084 ,  G06N7/01 ,  G06V10/267 ,  G06V10/454 ,  G06V10/751 ,  G06V10/763 ,  G06V10/764 ,  G06V10/7715 ,  G06V10/7784 ,  G06V10/82 ,  G06V10/993 ,  G06V20/69 ,  G16B40/00 ,  G06N5/046 ,  G06V20/47

Abstract: A system, a method and a non-transitory computer readable storage medium for base calling are described. The base calling method includes processing through a neural network first image data comprising images of clusters and their surrounding background captured by a sequencing system for one or more sequencing cycles of a sequencing run. The base calling method further includes producing a base call for one or more of the clusters of the one or more sequencing cycles of the sequencing run.

公开(公告)号：US12118096B2

公开(公告)日：2024-10-15

申请号：US17948648

申请日：2022-09-20

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Qiang Zhang ,  Bin Wang ,  Pengfei Wang ,  Yuandi Shi ,  Rongrong Chen ,  Xiaopeng Wei

IPC: G06F17/16 ,  G06F21/60 ,  G06N7/01

CPC classification number: G06F21/602 ,  G06F17/16 ,  G06N7/01

Abstract: The present disclosure discloses an image encryption method based on multi-scale compressed sensing and a Markov model. According to the difference in information carried by low-frequency coefficients and high-frequency coefficients of an image, different sampling rates are set for the low-frequency coefficients and the high-frequency coefficients of the image, which can effectively improve the reconstruction quality of a decrypted image. The decrypted image obtained by the present disclosure has higher quality than the decrypted image generated by the existing scheme, and a better visual effect and more complete original image information can be obtained.

公开(公告)号：US12112757B2

公开(公告)日：2024-10-08

申请号：US17720876

申请日：2022-04-14

Applicant: Tencent Technology (Shenzhen) Company Limited

Inventor： Na Li ,  Jun Wang

IPC: G10L17/04 ,  G06N3/04 ,  G06N3/045 ,  G06N3/08 ,  G06N3/084 ,  G06N7/01 ,  G10L17/02 ,  G10L17/14 ,  G10L17/18

CPC classification number: G10L17/04 ,  G06N3/04 ,  G06N3/045 ,  G06N3/08 ,  G06N3/084 ,  G06N7/01 ,  G10L17/02 ,  G10L17/14 ,  G10L17/18

Abstract: A voice identity feature extractor training method includes extracting a voice feature vector of training voice. The method may include determining a corresponding I-vector according to the voice feature vector of the training voice. The method may include adjusting a weight of a neural network model by using the I-vector as a first target output of the neural network model, to obtain a first neural network model. The method may include obtaining a voice feature vector of target detecting voice and determining an output result of the first neural network model for the voice feature vector of the target detecting voice. The method may include determining an I-vector latent variable. The method may include estimating a posterior mean of the I-vector latent variable, and adjusting a weight of the first neural network model using the posterior mean as a second target output, to obtain a voice identity feature extractor.