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公开(公告)号：US12124961B2
公开(公告)日：2024-10-22
申请号：US17133472
申请日：2020-12-23
Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor： Fearghal O'Donncha , Ambrish Rawat , Sean A. McKenna , Mathieu Sinn
Abstract: A computing device configured for automatic selection of model parameters includes a processor and a memory coupled to the processor. The memory stores instructions to cause the processor to perform acts including providing an initial set of model parameters and initial condition information to a model based on historical data. A model generates data based on the model parameters and the initial condition information. After determining whether the model-generated data is similar to an observed data, updated model parameters are selected for input to the model based on the determined similarity.
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公开(公告)号：US12118191B2
公开(公告)日：2024-10-15
申请号：US18401775
申请日：2024-01-02
Applicant: Truist Bank
Inventor： Alexis Pastore
IPC: G06F3/0484 , G06F3/0482 , G06F18/21 , G06F18/2321 , G06F18/2413 , G06F18/243 , G06N3/0442 , G06N3/045 , G06N3/0464 , G06N3/08 , G06N3/084 , G06N3/088 , G06N20/00 , G06N20/10 , G06Q20/22 , G06Q20/24 , G06Q20/32 , G06Q20/38 , G06Q20/40 , G06Q40/03
CPC classification number: G06F3/0484 , G06F3/0482 , G06Q20/227 , G06Q20/24 , G06Q40/03 , G06F18/217 , G06F18/2321 , G06F18/24143 , G06F18/24323 , G06N3/0442 , G06N3/045 , G06N3/0464 , G06N3/08 , G06N3/084 , G06N3/088 , G06N20/00 , G06N20/10 , G06Q20/3221 , G06Q20/389 , G06Q20/405
Abstract: A system and method for allowing a user to manage transactions in an online credit card application. The system includes a back-end server operating the online application and including a processor for processing data and information, a communications interface communicatively coupled to the processor, and a memory device storing data and executable code. When the code is executed, the processor can link one or more external bank accounts to the online application, provide a list of transactions that were made using the credit card, enable a user to select one or more of the transactions in the list to be paid independent of the other transactions, and enable the user to pay the selected transactions using the one or more external bank accounts.
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公开(公告)号：US12112270B2
公开(公告)日：2024-10-08
申请号：US17397249
申请日：2021-08-09
Applicant: Advanced Micro Devices, Inc.
Inventor： Nicholas Malaya
IPC: G06N3/084 , G06F18/21 , G06F18/214 , G06F18/28 , G06N3/04 , G06N3/088 , G06V10/764 , G06V10/774 , G06V10/82
CPC classification number: G06N3/084 , G06F18/2148 , G06F18/217 , G06F18/28 , G06N3/04 , G06N3/088 , G06V10/764 , G06V10/774 , G06V10/82
Abstract: A generator for generating artificial data, and training for the same. Data corresponding to a first label is altered within a reference labeled data set. A discriminator is trained based on the reference labeled data set to create a selectively poisoned discriminator. A generator is trained based on the selectively poisoned discriminator to create a selectively poisoned generator. The selectively poisoned generator is tested for the first label and tested for the second label to determine whether the generator is sufficiently poisoned for the first label and sufficiently accurate for the second label. If it is not, the generator is retrained based on the data set including the further altered data. The generator includes a first ANN to input first information and output a set of artificial data that is classifiable using a first label and not classifiable using a second label of the set of labeled data.
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公开(公告)号：US12111620B2
公开(公告)日：2024-10-08
申请号：US17029788
申请日：2020-09-23
Applicant: Tata Consultancy Services Limited
Inventor： Srinarayana Nagarathinam , Avinash Achar , Arunchandar Vasan
CPC classification number: G05B13/027 , F24F11/62 , G05B15/02 , G06N3/088
Abstract: Reinforcement Learning agent interacting with a real-world building to determine optimal policy may not be viable due to comfort constraints. Embodiments of the present disclosure provide multi-deep agent RL for dynamically controlling electrical equipment in buildings, wherein a simulation model is generated using design specification of (i) controllable electrical equipment (or subsystem) and (ii) building. Each RL agent is trained using simulation model and deployed in the subsystem. Reward function for each subsystem includes some portion of reward from other subsystem(s). Based on reward function of each RL agent, each RL agent learns an optimal control parameter during execution of RL agent in subsystem. Further, a global optimal control parameter list is generated using the optimal control parameter. The control parameters in the global optimal control parameters list are fine-tuned to improve subsystem's performance. Information on fine-tuning parameters of the subsystem and reward function are used for training RL agents.
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5.发明公开
公开(公告)号：US20240330690A1
公开(公告)日：2024-10-03
申请号：US17756849
申请日：2021-09-13
Applicant: ZHEJIANG UNIVERSITY
Inventor： Huajin Tang , Gehua Ma , Rui Yan
Abstract: A POI recommendation method and system based on brain-inspired spatiotemporal perceptual representation is provided. The method includes: constructing a POI context graph structure based on a POI check-in dataset; sampling a check-in sequence context graph, and training a POI check-in sequence embedding model in a brain-inspired spatiotemporal perceptual embedding model by unsupervised learning; sampling a spatial context graph and a spatiotemporal context graph to train a spatiotemporal embedding model in a brain-inspired spatiotemporal perceptual embedding model; combining a POI sequence representation vector and a POI spatiotemporal union representation vector into a POI spatiotemporal perceptual representation vector; training a recurrent neural network recommender based on the POI spatiotemporal perceptual representation vector; and recommending a next POI through the trained recurrent neural network recommender. By mining the spatiotemporal complexity and check-in sequences of POIs, the POI recommendation method and system enable efficient representation of POIs from multiple perspectives.
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公开(公告)号：US12093871B2
公开(公告)日：2024-09-17
申请号：US18128987
申请日：2023-03-30
Applicant: Senseye, Inc.
Inventor： David Zakariaie , Kathryn McNeil , Alexander Rowe , Joseph Brown , Patricia Herrmann , Jared Bowden , Taumer Anabtawi , Andrew R. Sommerlot , Seth Weisberg , Veronica Choi
IPC: G06K9/62 , A61B3/00 , A61B3/11 , A61B3/113 , A61B3/14 , A61B5/00 , A61B5/11 , A61B5/16 , G06N3/045 , G06N3/08 , G06Q10/0635 , G06Q10/0639 , G06Q10/10 , G06T7/73 , G06V10/143 , G06V10/44 , G06V10/764 , G06V20/40 , G06V40/18 , G06V40/19 , G16H15/00 , G16H30/20 , G16H50/20 , G16H50/50 , G16H50/70 , G06N3/088 , G16H50/30
CPC classification number: G06Q10/0635 , A61B3/0025 , A61B3/0041 , A61B3/0091 , A61B3/112 , A61B3/113 , A61B3/145 , A61B5/1103 , A61B5/161 , A61B5/163 , A61B5/165 , A61B5/4845 , A61B5/4863 , A61B5/6898 , A61B5/7246 , G06N3/045 , G06N3/08 , G06Q10/06398 , G06Q10/10 , G06T7/73 , G06V10/143 , G06V10/454 , G06V10/764 , G06V20/46 , G06V40/18 , G06V40/19 , G06V40/193 , G16H15/00 , G16H30/20 , G16H50/20 , G16H50/50 , G16H50/70 , A61B5/7267 , A61B2503/20 , G06N3/088 , G06T2207/10016 , G06T2207/20081 , G06T2207/20084 , G06T2207/30041 , G16H50/30
Abstract: A method to measure a cognitive load based upon ocular information of a subject includes the steps of: providing a video camera configured to record a close-up view of at least one eye of the subject; providing a computing device electronically connected to the video camera and the electronic display; recording, via the video camera, the ocular information of the at least one eye of the subject; processing, via the computing device, the ocular information to identify changes in ocular signals of the subject through the use of convolutional neural networks; evaluating, via the computing device, the changes in ocular signals from the convolutional neural networks by a machine learning algorithm; determining, via the machine learning algorithm, the cognitive load for the subject; and displaying, to the subject and/or to a supervisor, the cognitive load for the subject.
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公开(公告)号：US12093835B2
公开(公告)日：2024-09-17
申请号：US17971601
申请日：2022-10-23
Applicant: BLINK TECHNOLOGIES INC.
Inventor： Oren Haimovitch-Yogev , Tsahi Mizrahi , Andrey Zhitnikov , Almog David , Artyom Borzin , Gilad Drozdov
IPC: G06N3/088 , G06F18/214 , G06N3/04 , G06N3/0455 , G06N3/0464 , G06N3/08 , G06N20/10 , G06T7/11 , G06V10/75 , G06V10/764 , G06V10/82 , G06V40/16 , G06V40/18 , G06V40/19
CPC classification number: G06N3/088 , G06F18/2155 , G06N3/04 , G06N3/08 , G06T7/11 , G06V10/755 , G06V10/82 , G06V40/165 , G06V40/171 , G06V40/19 , G06V40/193 , G06T2207/20081 , G06T2207/20084 , G06T2207/20132
Abstract: Unsupervised, deep learning of eye-landmarks in a user-specific eyes' image data by capturing an unlabeled image comprising an eye region of a user, using an initial geometrically regularized loss function, training a plurality of convolutional autoencoders on the unlabeled image comprising the eye region of the user to recover a plurality of user-specific eye landmarks, training a convolutional neural network for autoencoded landmarks-based recovery from the unlabeled image, and where the initial geometrically regularized loss function is represented by the formula LAE=λreconLrecon+λconcLconc+λsepLsep+λeqvLeqv where LAE is total AutoEncoder Loss, λreconLrecon is λ-weighted reconstruction loss, λconcLconce is λ-weighted concentration loss, λsepLsep is λ-weighted separation loss, and λeqvLeqv is λ-weighted equivalence loss.
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公开(公告)号：US12093834B2
公开(公告)日：2024-09-17
申请号：US17762345
申请日：2020-09-22
Applicant: VayaVision Sensing Ltd.
Inventor： Youval Nehmadi , Shahar Ben Ezra , Shmuel Mangan , Mark Wagner , Anna Cohen , Itzik Avital
IPC: G06N3/088 , G06V10/25 , G06V10/764 , G06V10/774 , G06V10/776 , G06V10/82 , G06V20/58
CPC classification number: G06N3/088 , G06V10/25 , G06V10/764 , G06V10/774 , G06V10/776 , G06V10/82 , G06V20/58
Abstract: A perception system, comprising a set of reference sensors; a set of test sensors and a computing device, which is configured for receiving first training signals from the set of reference sensors and receiving second training signals from the set of test sensors, the set of reference sensors and the set of test sensors simultaneously exposed to a common scene; processing the first training signals to obtain reference images containing reference depth information associated with the scene; and using the second training signals and the reference images to train a neural network for transforming subsequent test signals from the set of test sensors into test images containing inferred depth information.
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公开(公告)号：US12093804B2
公开(公告)日：2024-09-17
申请号：US17255267
申请日：2020-02-18
Inventor： Dechang Pi , Junfu Chen , Zhiyuan Wu
CPC classification number: G06N3/045 , G06N3/084 , G06N3/088 , H04B7/18582
Abstract: The present disclosure provides a satellite anomaly detection method and system for an adversarial network auto-encoder. The method includes: obtaining a variational auto-encoder and a generative adversarial network; adding the generative adversarial network to the variational auto-encoder, and determining an optimized variational auto-encoder; obtaining to-be-detected satellite telemetry data; and determining a current operating status of a satellite based on the to-be-detected satellite telemetry data by using the optimized variational auto-encoder, where the current operating status includes a normal operating state or an abnormal operating state. The satellite anomaly detection method and system for an adversarial network according to the present disclosure solve the low accuracy problem of automatic satellite anomaly detection in the prior art.
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公开(公告)号：US20240303494A1
公开(公告)日：2024-09-12
申请号：US18666613
申请日：2024-05-16
Applicant: NVIDIA Corporation
Inventor： Ming-Yu LIU , Xun HUANG , Tero Tapani KARRAS , Timo AILA , Jaakko LEHTINEN
IPC: G06N3/088 , G06F18/214 , G06F18/2431 , G06T3/02 , G06T3/60 , G06T7/73 , G06V10/764 , G06V10/82
CPC classification number: G06N3/088 , G06F18/214 , G06F18/2431 , G06T3/02 , G06T3/60 , G06T7/74 , G06V10/764 , G06V10/82 , G06T2207/20081 , G06T2207/20084
Abstract: A few-shot, unsupervised image-to-image translation (“FUNIT”) algorithm is disclosed that accepts as input images of previously-unseen target classes. These target classes are specified at inference time by only a few images, such as a single image or a pair of images, of an object of the target type. A FUNIT network can be trained using a data set containing images of many different object classes, in order to translate images from one class to another class by leveraging few input images of the target class. By learning to extract appearance patterns from the few input images for the translation task, the network learns a generalizable appearance pattern extractor that can be applied to images of unseen classes at translation time for a few-shot image-to-image translation task.
