公开(公告)号：US11631022B2

公开(公告)日：2023-04-18

申请号：US16351794

申请日：2019-03-13

申请人： Azimuth1, LLC

发明人： Jason R. Dalton ,  Anna M. Harrington

IPC分类号： G06F30/27 ,  G06N7/01 ,  G06T11/20 ,  G06N20/10 ,  G06T17/05 ,  G06N20/00

摘要： Soil and groundwater contamination migration are forecasted according to instructions stored in a memory and executable by a processor to facilitate prompt and accurate remediation efforts. In embodiments, an environmental machine learning model is employed, and analysis and determination of contaminant plume distances, sources and destinations are made. A database stores raw environmental site data, from which relevant data can be extracted for a site of interest, and the environmental machine learning model can be trained on the extracted relevant data to predict the spatial and cross-section probability distribution of a contaminant plume at the site of interest.

公开(公告)号：US11630314B2

公开(公告)日：2023-04-18

申请号：US17719885

申请日：2022-04-13

申请人： Magic Leap, Inc.

发明人： Adrian Kaehler ,  Gary R. Bradski ,  Vijay Badrinarayanan

IPC分类号： G06F3/01 ,  G02B27/01 ,  G06N3/10 ,  G06V20/10 ,  G06V20/20 ,  G06V40/20 ,  G06F18/214 ,  G06F18/00 ,  G06N3/044 ,  G06N3/045 ,  G06N3/048 ,  G06T7/70 ,  G06F3/0346 ,  G06F3/04815 ,  G06N3/08 ,  G06T19/00 ,  G06N5/025 ,  G06V10/44 ,  G06N3/047 ,  G06N5/01 ,  G06N7/01

摘要： An example wearable display system can be capable of determining a user interface (UI) event with respect to a virtual UI device (e.g., a button) and a pointer (e.g., a finger or a stylus) using a neural network. The wearable display system can render a representation of the UI device onto an image of the pointer captured when the virtual UI device is shown to the user and the user uses the pointer to interact with the virtual UI device. The representation of the UI device can include concentric shapes (or shapes with similar or the same centers of gravity) of high contrast. The neural network can be trained using training images with representations of virtual UI devices and pointers.

公开(公告)号：US11625644B1

公开(公告)日：2023-04-11

申请号：US16793233

申请日：2020-02-18

申请人： Amazon Technologies, Inc.

发明人： Elad Haramaty ,  Liane Lewin-Eytan ,  David Carmel ,  Arnon Lazerson

IPC分类号： G06N20/00 ,  G06F16/2457 ,  G06N7/01

摘要： Devices and techniques are generally described for ranking of search results based on multiple objectives. In various examples, a first set of search results may be determined. A first objective and a second objective for ranking the first set of search results may be determined. A first label associated with the first objective may be selected for a first training data instance. A second label associated with the second objective may be selected for a second training data instance. A first machine learning model may be generated using the first training data instance and the second training data instance. In some examples, the first machine learning model may be effective to rank the first set of search results based at least in part on the first objective and the second objective.

公开(公告)号：US20230100213A1

公开(公告)日：2023-03-30

申请号：US17954059

申请日：2022-09-27

申请人： ROYAL BANK OF CANADA

发明人： Chandramouli Shama SASTRY ,  Alexander Radomir Branislav RADOVIC ,  Marcus Anthony BRUBAKER ,  Andreas Steffen Michael LEHRMANN

IPC分类号： G06N3/09 ,  G06N7/01

摘要： A computer-implemented system and method for estimating a Cumulative Distribution Function (CDF) are provided. The method includes: receive input data representing a volume V of a target space indicating a future target event; compute, using the trained neural network, an estimation of a first flux through a boundary of the volume V; compute, using the trained neural network, an estimation of a second flux through a boundary of a volume W of a base space based on the estimation of the first flux through the boundary of the volume V; generate, using the trained neural network, an estimation of a CDF for the volume V based on the second flux through the boundary of the volume W; compute a probability for the future target event based on the estimated CDF for the volume V; and generate a control command based on the probability for the future target event.

公开(公告)号：US20230088974A1

公开(公告)日：2023-03-23

申请号：US18052731

申请日：2022-11-04

申请人： Spass Inc.

发明人： yong hwan kim ,  Jae Bum LEE

IPC分类号： G16H40/63 ,  G06N7/01

摘要： A method, device, and computer program for predicting occurrence of patient shock using artificial intelligence are provided. The method for predicting occurrence of patient shock using artificial intelligence according to various embodiments of the present invention is a method performed by a computing device, the method comprising the steps of: collecting biometric data of a patient; extracting one or more feature values from the collected biometric data; and determining the possibility of occurrence of a medical event with respect to the patient using the extracted one or more feature values.

公开(公告)号：US11610689B2

公开(公告)日：2023-03-21

申请号：US16269639

申请日：2019-02-07

申请人： AJOU UNIVERSITY INDUSTRY—ACADEMIC COOPERATION FOUNDATION

发明人： O Kyu Noh

IPC分类号： G06N20/00 ,  G06N7/00 ,  G16H50/70 ,  G06N3/126 ,  G06N7/01

摘要： Provided are a method for adjusting a continuous variable, a method and an apparatus for analyzing a correlation using the same. A method for adjusting a continuous variable according to an exemplary embodiment of the present disclosure is a method for adjusting a continuous variable by an apparatus including: determining at least one confounder from analysis data; classifying the analysis data into a plurality of subgroups having the same combination of confounders; and generating a new continuous variable for each subgroup based on a representative value of a continuous variable distribution.

公开(公告)号：US11584020B2

公开(公告)日：2023-02-21

申请号：US16702518

申请日：2019-12-03

申请人： CloudMinds Robotics Co., Ltd.

发明人： William Xiao-Qing Huang ,  Shikui Ma ,  Karl Zhao ,  Zhenping Guo ,  Qiulin Wang

IPC分类号： G05B19/418 ,  B25J13/00 ,  B25J9/00 ,  G05D1/00 ,  G01C21/20 ,  B25J9/16 ,  G06N5/022 ,  G06N3/044 ,  G06N3/045 ,  G06N7/01

摘要： A human augmented robotics intelligence operation system can include a plurality of robots, each robot having a plurality of sensors; a robot control unit; and one or more articulating joints; a cloud-based robotic intelligence engine having; a communication module; a historical database; and a processor; and a human augmentation platform. The processor can be configured to make a probabilistic determination regarding the likelihood of successfully completing the particular user command. When the probabilistic determination is above a pre-determined threshold, the processor sends necessary executable commands to the robot control unit. Alternatively, when the probabilistic determination is below the predetermined threshold, the processor generates an alert and flags the operation for human review.

公开(公告)号：US20230135121A1

公开(公告)日：2023-05-04

申请号：US18091702

申请日：2022-12-30

申请人： State Farm Mutual Automobile Insurance Company

发明人： Carlee A. Clymer ,  Gary Foreman ,  Ronald R. Duehr ,  Denson Smith ,  Vincent M. Hummel ,  Bradley J Walder ,  Chad Mychal Hirst ,  Justin Devore ,  Shane Tomlinson ,  David A Pluimer ,  Pavan Kumar Bhagavatula ,  John Westhues ,  Tracey Leigh Knorr ,  Erin E. Miller ,  Joshua T. Monk ,  Aaron Ames ,  John G. McConkey ,  Michael Cicilio Fresquez ,  Himanshu Chhita ,  Jason Beckman ,  Douglas A. Graff ,  Michele Wittman ,  Alexis Cates ,  Stephen Young ,  Rajesh Panicker ,  Yohan Santos ,  Stephen Wilson ,  Carrie A Read ,  Michael Brown ,  Robin A Rose

IPC分类号： G06Q40/08 ,  G06N7/01 ,  G07C5/08

摘要： A method of identifying a vehicle total loss claim includes retrieving a plurality of historical vehicle records, labeling the records as repaired or total loss, calculating mean cost values, training a regression model, optimizing a probability threshold, analyzing a plurality of inputs to generate a prediction, and transmitting the prediction. A computing system includes a transceiver; a processor; and a memory storing instructions that, when executed by the processor, cause the computing system to receive answers, transmit the answers, receive a prediction, when the prediction is repairable, generate a repair suggestion, and when the prediction is total loss, generate a settlement offer. A non-transitory computer readable medium containing program instructions that when executed, cause a computer to receive answers, transmit the answers, receive a prediction, when the prediction is repairable, generate a repair suggestion, and when prediction is total loss, generate a settlement offer.

公开(公告)号：US11640427B2

公开(公告)日：2023-05-02

申请号：US17207714

申请日：2021-03-21

申请人： Jong Sik Yoon

发明人： Jong Sik Yoon

IPC分类号： G10L15/26 ,  G06F16/783 ,  G06F40/35 ,  H04N21/25 ,  G06Q30/0601 ,  G06F16/738 ,  G06N7/01

摘要： Disclosed is a method for recommending a video by a video recommendation system, comprising: collecting and storing in a database of the video recommendation system videos related to products being sold and video information of the videos; converting voice included in each of the videos to text; obtaining words from the converted text and a time stamp for each of the words; extracting noun keywords in the text and identifying frequencies of the noun keywords, by analyzing morphemes of the text; performing a sentiment analysis on sentences composed of the words in the text; receiving a selection of one of the products; identifying videos associated with the selected product from among the videos stored in the database based on the noun keywords and the frequencies of the noun keywords; providing videos according to a predetermined criterion among the identified videos, based on a result of the sentiment analysis; and if one of the provided videos is selected, providing a partial video in a time section associated with the selected product.

公开(公告)号：US20230128941A1

公开(公告)日：2023-04-27

申请号：US18045382

申请日：2022-10-10

申请人： Robert Bosch GmbH

发明人： Felix Schmitt

IPC分类号： G06N3/08 ,  G06N7/01

摘要： A method for controlling an agent. The method includes training a neural network using training data that contain, for a multiplicity of agents, examples of a behavior of the agents, the output of the neural network including a prediction of a behavior and being a function of network parameters that are trained in common for all training data, and being a function of a further parameter that is trained individually for each of the agents of the multiplicity of agents; fitting of a probability distribution to the values of the further parameter for the agents that result from the training; sampling a value from the probability distribution for a further agent in the environment of the agent; and controlling the agent, taking into account a prediction of the behavior of the further agent that the neural network outputs for the sampled value for the further agent.