公开(公告)号：US20240354937A1

公开(公告)日：2024-10-24

申请号：US18502867

申请日：2023-11-06

申请人： SpIntellx, Inc.

发明人： Akif Burak Tosun ,  Srinivas Chakra Chennubhotla ,  Jeffrey Louis Fine

IPC分类号： G06T7/00 ,  G06F18/2431 ,  G06V10/778 ,  G06V20/69 ,  G16H30/40

CPC分类号： G06T7/0012 ,  G06F18/2431 ,  G06V10/7784 ,  G06V20/69 ,  G06V20/695 ,  G06V20/698 ,  G16H30/40 ,  G06T2200/24 ,  G06T2207/20081 ,  G06T2207/30016 ,  G06T2207/30024 ,  G06T2207/30061 ,  G06T2207/30068 ,  G06T2207/30096 ,  G06T2207/30242

摘要： Pathologists are adopting digital pathology for diagnosis, using whole slide images (WSIs). Explainable AI (xAI) is a new approach to AI that can reveal underlying reasons for its results. As such, xAI can promote safety, reliability, and accountability of machine learning for critical tasks such as pathology diagnosis. HistoMapr provides intelligent xAI guides for pathologists to improve the efficiency and accuracy of pathological diagnoses. HistoMapr can previews entire pathology cases' WSIs, identifies key diagnostic regions of interest (ROIs), determines one or more conditions associated with each ROI, provisionally labels each ROI with the identified conditions, and can triages them. The ROIs are presented to the pathologist in an interactive, explainable fashion for rapid interpretation. The pathologist can be in control and can access xAI analysis via a “why?” interface. HistoMapr can track the pathologist's decisions and assemble a pathology report using suggested, standardized terminology.

公开(公告)号：US12056590B2

公开(公告)日：2024-08-06

申请号：US16541022

申请日：2019-08-14

申请人： CAPITAL ONE SERVICES, LLC

发明人： Chi-San Ho ,  Micah Price ,  Yue Duan

IPC分类号： G06N20/20 ,  G06N5/02 ,  G06N20/00 ,  G06V10/764 ,  G06V10/778 ,  G06V10/94 ,  G06V20/10 ,  G06F17/18

CPC分类号： G06N20/20 ,  G06N5/02 ,  G06N20/00 ,  G06V10/764 ,  G06V10/7784 ,  G06V10/95 ,  G06V20/10 ,  G06F17/18

摘要： Systems and methods for clustering data are disclosed. For example, a system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a first client device, a classification data associated with a classification model and receiving feature data corresponding to the classification model output. The operations may include training a meta-model to predict the classification data based on the feature data and/or additional data associated with the classification data such as location data or environmental data. The operations may include generating a meta-model output based on the classification data, the feature data, and/or the additional data. The operations may include updating the classification model based on the meta-model output and transmitting the updated classification model to at least one of the first client device or a second client device.

公开(公告)号：US20240257184A1

公开(公告)日：2024-08-01

申请号：US18627869

申请日：2024-04-05

申请人： 7-Eleven, Inc.

发明人： Matthew Raymond Magee ,  Shahmeer Ali Mirza

IPC分类号： G06Q30/0251 ,  G06N20/00 ,  G06Q30/0601 ,  G06V10/778 ,  G06V10/82 ,  G06V20/10 ,  G06V20/52

CPC分类号： G06Q30/0261 ,  G06N20/00 ,  G06Q30/0633 ,  G06V10/7784 ,  G06V10/82 ,  G06V20/10 ,  G06V20/52

摘要： An apparatus includes a display and a processor. The processor displays a virtual shopping cart. The processor also receives information indicating that an algorithm determined that a physical item was selected by a person during a shopping session in a physical store, based on a set of inputs received from sensors located within the store. In response, the processor displays a virtual item, which includes a graphical representation of the physical item. The processor additionally displays a rack video captured during the shopping session by a rack camera located in the store. The rack camera is directed at a physical rack located in the store, which includes the physical item. In response to displaying the rack video, the processor receives information identifying the virtual item, where the rack video depicts that the person selected the physical item. The processor then stores the virtual item in the virtual shopping cart.

公开(公告)号：US11981352B2

公开(公告)日：2024-05-14

申请号：US17190631

申请日：2021-03-03

申请人： Perceptive Automata, Inc.

发明人： Samuel English Anthony ,  Kshitij Misra ,  Avery Wagner Faller

IPC分类号： G06N3/04 ,  B60W30/00 ,  B60W60/00 ,  G05D1/00 ,  G06F18/214 ,  G06F18/40 ,  G06N3/08 ,  G06N3/084 ,  G06V10/778 ,  G06V20/40 ,  G06V20/58 ,  G06V40/20 ,  G08G1/04 ,  G08G1/16 ,  G06N5/01 ,  G06N20/10

CPC分类号： B60W60/00274 ,  B60W30/00 ,  G05D1/0088 ,  G06F18/214 ,  G06F18/41 ,  G06N3/04 ,  G06N3/08 ,  G06N3/084 ,  G06V10/7784 ,  G06V20/41 ,  G06V20/58 ,  G06V40/20 ,  G08G1/04 ,  G08G1/166 ,  G05D2201/0213 ,  G06N5/01 ,  G06N20/10

摘要： Systems and methods for predicting user interaction with vehicles. A computing device receives an image and a video segment of a road scene, the first at least one of an image and a video segment being taken from a perspective of a participant in the road scene and then generates stimulus data based on the image and the video segment. Stimulus data is transmitted to a user interface and response data is received, which includes at least one of an action and a likelihood of the action corresponding to another participant in the road scene. The computing device aggregates a subset of the plurality of response data to form statistical data and a model is created based on the statistical data. The model is applied to another image or video segment and a prediction of user behavior in the another image or video segment is generated.

公开(公告)号：US20230409866A1

公开(公告)日：2023-12-21

申请号：US18204239

申请日：2023-05-31

申请人： Strong Force IoT Portfolio 2016, LLC

发明人： Charles Howard Cella ,  Gerald William Duffy, JR. ,  Jeffrey P. McGuckin ,  Mehul Desai

IPC分类号： G06N3/006 ,  G05B19/418 ,  H04L67/12 ,  H04L1/00 ,  H04B17/309 ,  G06N5/046 ,  G05B23/02 ,  G06N3/02 ,  H04B17/318 ,  H04L67/1097 ,  G05B13/02 ,  G06Q30/02 ,  H04W4/70 ,  G06Q30/06 ,  H04W4/38 ,  H04B17/345 ,  H04B17/23 ,  G16Z99/00 ,  G01M13/028 ,  G01M13/04 ,  G01M13/045 ,  G06N3/084 ,  G06N3/088 ,  G06Q10/04 ,  G06Q10/0639 ,  G06Q50/00 ,  H04L1/18 ,  H04L1/1867 ,  H03M1/12 ,  G06F18/21 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N7/01 ,  G06V10/778 ,  H02M1/12 ,  G06N20/00

CPC分类号： G06N3/006 ,  G05B19/41875 ,  G05B19/4185 ,  H04L67/12 ,  G05B19/41865 ,  G05B19/4183 ,  H04L1/0002 ,  H04B17/309 ,  G06N5/046 ,  G05B23/0221 ,  G06N3/02 ,  G05B23/0294 ,  H04B17/318 ,  G05B19/41845 ,  G05B23/0283 ,  G05B23/0229 ,  H04L67/1097 ,  G05B13/028 ,  G05B23/0289 ,  G05B23/0291 ,  G05B19/4184 ,  G05B23/0297 ,  G05B23/0264 ,  G05B23/0286 ,  G05B23/024 ,  G06Q30/02 ,  H04W4/70 ,  G06Q30/06 ,  H04W4/38 ,  H04B17/345 ,  H04B17/23 ,  G16Z99/00 ,  G01M13/028 ,  G01M13/04 ,  G01M13/045 ,  G06N3/084 ,  G06N3/088 ,  G06Q10/04 ,  G06Q10/0639 ,  G06Q30/0278 ,  G06Q50/00 ,  H04L1/0041 ,  H04L1/18 ,  H04L1/1874 ,  H03M1/12 ,  G06F18/2178 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N7/01 ,  G06V10/7784 ,  H02M1/12 ,  G06N20/00 ,  G05B2219/32287 ,  H04L5/0064

摘要： Systems for self-organizing data collection and storage in a manufacturing environment are disclosed. A system may include a data collector for handling a plurality of sensor inputs from sensors in the manufacturing system, wherein the plurality of sensor inputs is configured to sense at least one of: an operational mode, a fault mode, a maintenance mode, or a health status of at least one target system. The system may also include a self-organizing system for self-organizing a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs. The self-organizing system may organize a swarm of mobile data collectors to collect data from a plurality of target systems.

公开(公告)号：US11847822B2

公开(公告)日：2023-12-19

申请号：US17052035

申请日：2019-03-08

申请人： SONY CORPORATION

发明人： Masato Nishio ,  Yuhei Yabe ,  Tomoo Mizukami

IPC分类号： G06V10/778 ,  G06V20/10 ,  G06F18/40 ,  G06F18/21

CPC分类号： G06V10/7784 ,  G06F18/217 ,  G06F18/41 ,  G06V10/7788 ,  G06V20/10

摘要： An information processing device is provided that includes an operation control unit which controls the operations of an autonomous mobile object that performs an action according to a recognition operation. Based on the detection of the start of teaching related to pattern recognition learning, the operation control unit instructs the autonomous mobile object to obtain information regarding the learning target that is to be learnt in a corresponding manner to a taught label. Moreover, an information processing method is provided that is implemented in a processor and that includes controlling the operations of an autonomous mobile object which performs an action according to a recognition operation. Based on the detection of the start of teaching related to pattern recognition learning, the controlling of the operations includes instructing the autonomous mobile object to obtain information regarding the learning target that is to be learnt in a corresponding manner to a taught label.

公开(公告)号：US11830240B2

公开(公告)日：2023-11-28

申请号：US18089475

申请日：2022-12-27

申请人： SAMSUNG DISPLAY CO., LTD.

发明人： Janghwan Lee

IPC分类号： G06V10/778 ,  G06N20/20 ,  G06V10/82

CPC分类号： G06V10/7784 ,  G06N20/20 ,  G06V10/82

摘要： A system includes a memory; and a processor configured to train a first machine learning model based on the first dataset labeling; provide the second dataset to the trained first machine learning model to generate an updated second dataset including an updated second dataset labeling, determine a first difference between the updated second dataset labeling and the second dataset labeling; train a second machine learning model based on the updated second dataset labeling if the first difference is greater than a first threshold value; provide the first dataset to the trained second machine learning model to generate an updated first dataset including an updated first dataset labeling, determine a second difference between the updated first dataset labeling and the first dataset labeling; and train the first machine learning model based on the updated first dataset labeling if the second difference is greater than a second threshold value.

公开(公告)号：US11810341B2

公开(公告)日：2023-11-07

申请号：US17145271

申请日：2021-01-08

申请人： Robert Bosch GmbH

发明人： Andres Mauricio Munoz Delgado

IPC分类号： G06V10/82 ,  G06N3/04 ,  G06N3/08 ,  G06F18/21 ,  G06F18/22 ,  G06F18/23 ,  G06F18/2413 ,  G06V10/774 ,  G06V10/778

CPC分类号： G06V10/82 ,  G06F18/217 ,  G06F18/22 ,  G06F18/23 ,  G06F18/2413 ,  G06N3/04 ,  G06N3/08 ,  G06V10/774 ,  G06V10/7784

摘要： A computer-implemented method of identifying filters for use in determining explainability of a trained neural network. The method comprises obtaining a dataset comprising the input image and an annotation of an input image, the annotation indicating at least one part of the input image which is relevant for inferring classification of the input image, determining an explanation filter set by iteratively: selecting a filter of the plurality of filters; adding the filter to the explanation filter set; computing an explanation heatmap for the input image by resizing and combining an output of each filter in the explanation filter set to obtain the explanation heatmap, the explanation heatmap having a spatial resolution of the input image; and computing a similarity metric by comparing the explanation heatmap to the annotation of the input image; until the similarity metric is greater than or equal to a similarity threshold; and outputting the explanation filter set.

公开(公告)号：US11790638B2

公开(公告)日：2023-10-17

申请号：US17674920

申请日：2022-02-18

申请人： Bank of America Corporation

发明人： Stephen T. Shannon ,  James Alexander ,  Brian J. Smith

IPC分类号： G06V40/16 ,  G06V10/774 ,  G08B25/01 ,  G06N20/00 ,  G06F18/21 ,  G06V10/778

CPC分类号： G06V10/774 ,  G06F18/217 ,  G06N20/00 ,  G06V10/7784 ,  G06V40/172 ,  G08B25/014

摘要： Aspects of the disclosure relate to monitoring devices at enterprise locations using machine-learning models to protect enterprise-managed information and resources. In some embodiments, a computing platform may receive, from one or more data source computer systems, passive monitoring data. Based on applying a machine-learning classification model to the passive monitoring data received from the one or more data source computer systems, the computing platform may determine to trigger a data capture process at an enterprise center. In response to determining to trigger the data capture process, the computing platform may initiate an active monitoring process to capture event data at the enterprise center. Thereafter, the computing platform may generate one or more alert messages based on the event data captured at the enterprise center. Then, the computing platform may send the one or more alert messages to one or more enterprise computer systems.

公开(公告)号：US11776150B2

公开(公告)日：2023-10-03

申请号：US17385762

申请日：2021-07-26

申请人： GE Precision Healthcare LLC

发明人： Khaled Salem Younis ,  Ravi Soni ,  Katelyn Rose Nye ,  Gireesha Chinthamani Rao ,  John Michael Sabol ,  Yash N. Shah

IPC分类号： G06K9/00 ,  G06T7/70 ,  G16H30/20 ,  G16H30/40 ,  G06N3/08 ,  G06T7/00 ,  G06F18/21 ,  G06F18/214 ,  G06F18/2413 ,  G06F18/2431 ,  G06V10/764 ,  G06V10/778 ,  G06V10/82 ,  G06V20/00

CPC分类号： G06T7/70 ,  G06F18/217 ,  G06F18/2155 ,  G06F18/2413 ,  G06F18/2431 ,  G06N3/08 ,  G06T7/0012 ,  G06V10/764 ,  G06V10/7784 ,  G06V10/82 ,  G06V20/00 ,  G16H30/20 ,  G16H30/40 ,  G06T2207/10116 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06V2201/03

摘要： An x-ray image laterality detection system is provided. The x-ray image laterality detection system includes a detection computing device. The processor of the computing device is programmed to execute a neural network model for analyzing x-ray images, wherein the neural network model is trained with training x-ray images as inputs and observed laterality classes associated with the training x-ray images as outputs. The process is also programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign a laterality class to the unclassified x-ray image. If the assigned laterality class is not target laterality, the processor is programmed to adjust the unclassified x-ray image to derive a corrected x-ray image having the target laterality and output the corrected x-ray image. If the assigned laterality class is the target laterality, the processor is programmed to output the unclassified x-ray image.