公开(公告)号：US20240365128A1

公开(公告)日：2024-10-31

申请号：US18771435

申请日：2024-07-12

申请人： Digital Global Systems, Inc.

发明人： Armando Montalvo ,  Edward Hummel ,  Dwight Inman

IPC分类号： H04W16/10 ,  G06F30/27 ,  G06N3/02 ,  G06N3/042 ,  G06N3/045 ,  G06N5/022 ,  G06N5/04 ,  G06N20/00 ,  G06N20/10 ,  G06N20/20 ,  H04L41/0893 ,  H04L41/0894 ,  H04W16/14 ,  H04W24/02 ,  H04W24/08 ,  H04W72/0453

CPC分类号： H04W16/10 ,  G06F30/27 ,  G06N3/02 ,  G06N5/022 ,  G06N5/04 ,  G06N20/00 ,  G06N20/10 ,  G06N20/20 ,  H04L41/0893 ,  H04W24/02 ,  H04W72/0453 ,  G06N3/042 ,  G06N3/045 ,  H04L41/0894 ,  H04W16/14 ,  H04W24/08

摘要： Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.

公开(公告)号：US20240362736A1

公开(公告)日：2024-10-31

申请号：US18770899

申请日：2024-07-12

申请人： Charles Isgar

发明人： Charles Isgar

IPC分类号： G06Q50/60 ,  G06F16/9535 ,  G06F16/955 ,  G06K19/06 ,  G06N5/04

CPC分类号： G06Q50/60 ,  G06F16/9535 ,  G06F16/9554 ,  G06K19/06009 ,  G06N5/04

摘要： Described is a digitized mail control system. The system may include a server having a memory storing mail data associated with a user and a user computing device coupled to the server. The server may be programmed to receive an access signal from the user computing device that has accessed the system. In response to receiving the access signal, the server may process the user data and locate the stored mail data associated with the user data. The server may then create and send for execution on the user computing device instruction to display mail data including sender information and action buttons associated with predetermined actions to take in response to the sender information. The server may receive a signal including a selected action associated with a selected action button with regard to a selected sender and automatically execute program code corresponding to the selected action.

公开(公告)号：US20240362562A1

公开(公告)日：2024-10-31

申请号：US18141072

申请日：2023-04-28

申请人： Copperleaf Technologies Inc.

发明人： Balázs János KERESZTES ,  Stanley Thomas COLEMAN

IPC分类号： G06Q10/0635 ,  G06N5/04

CPC分类号： G06Q10/0635 ,  G06N5/04

摘要： A method, apparatus and system for performing asset lifecycle modelling. The model is used to test various asset intervention hypotheses to generate a hazard rate and an intervention rate that may be used to minimize hazard risk across a system comprising assets that fail over their lifetimes.

公开(公告)号：US20240362425A1

公开(公告)日：2024-10-31

申请号：US18765256

申请日：2024-07-06

申请人： Kognitos, Inc.

发明人： Siddharth Agrawal ,  Aaron Dean BROWN ,  Binny Sher Gill ,  Ziv Kennan ,  Rupesh Mehta ,  Mohsin Mumtaz

IPC分类号： G06F40/40 ,  G06F8/33 ,  G06N5/022 ,  G06N5/04

CPC分类号： G06F40/40 ,  G06F8/33 ,  G06N5/022 ,  G06N5/04

摘要： Disclosed is an improved approach to implement an improved approach to retrospectively examiner and edit facts for an automation run. The user can then continue processing from the point of the fact change, rather than being required to restart the entire process from the very beginning.

公开(公告)号：US20240362097A1

公开(公告)日：2024-10-31

申请号：US18308216

申请日：2023-04-27

申请人： Dell Products L.P.

发明人： DEEPAGANESH PAULRAJ ,  MIN GONG ,  ASHOK NARAYANAN POTTI ,  DALE WANG

IPC分类号： G06F11/00 ,  G06N5/04

CPC分类号： G06F11/008 ,  G06F11/004 ,  G06N5/04 ,  G06F2201/86

摘要： Methods and systems for managing data processing systems are disclosed. A data processing system may include and depend on the operation of hardware and/or software components. To manage the operation of the data processing system, a data processing system manager may obtain logs for components of the data processing system. Inference models may be implemented to predict likely future component failures (e.g., failure sequences) and their associated times-to-failures using information recorded in the logs. The failure sequences may be presented as an acyclic graph that associates component failures, their times-to-failure, and related actions. The probable failure sequences may be analyzed to identify sets of actions. The sets of actions may be optimized based on deviations detected during executing the sets of actions to optimize operational goals (e.g., maximizing system lifetime, minimizing system costs), and/or reduce the likelihood of the data processing system becoming impaired.

公开(公告)号：US12131264B2

公开(公告)日：2024-10-29

申请号：US16938436

申请日：2020-07-24

申请人： Optum Services (Ireland) Limited

发明人： Lorcan B. MacManus ,  Peter Cogan ,  Conor Breen

IPC分类号： G06N5/04 ,  G06N20/00

CPC分类号： G06N5/04 ,  G06N20/00

摘要： There is a need for more effective and efficient predictive data analysis based at least in part on categorical input data. This need can be addressed by, for example, solutions for performing predictive data analysis that utilize at least one of categorical level merging, mutual-information-based feature filtering, feature-correlation-based feature filtering to generate training data feature value arrangements, as well as training and using categorical input machine learning models trained using the training data feature value arrangements.

公开(公告)号：US12131237B2

公开(公告)日：2024-10-29

申请号：US17930827

申请日：2022-09-09

申请人： BLUVECTOR, INC.

发明人： Scott B. Miserendino ,  Robert H. Klein ,  Ryan V. Peters ,  Peter E. Kaloroumakis

IPC分类号： G06F21/00 ,  G06F21/56 ,  G06N5/04 ,  G06N20/00 ,  H04W12/128 ,  G06F11/30 ,  H04L9/40

CPC分类号： G06N20/00 ,  G06F21/56 ,  G06F21/566 ,  G06N5/04 ,  H04W12/128 ,  G06F11/3006 ,  G06F2221/033 ,  G06F2221/2115 ,  H04L63/1433

摘要： A system and method for batched, supervised, in-situ machine learning classifier retraining for malware identification and model heterogeneity. The method produces a parent classifier model in one location and providing it to one or more in-situ retraining system or systems in a different location or locations, adjudicates the class determination of the parent classifier over the plurality of the samples evaluated by the in-situ retraining system or systems, determines a minimum number of adjudicated samples required to initiate the in-situ retraining process, creates a new training and test set using samples from one or more in-situ systems, blends a feature vector representation of the in-situ training and test sets with a feature vector representation of the parent training and test sets, conducts machine learning over the blended training set, evaluates the new and parent models using the blended test set and additional unlabeled samples, and elects whether to replace the parent classifier with the retrained version.

公开(公告)号：US20240357022A1

公开(公告)日：2024-10-24

申请号：US18762098

申请日：2024-07-02

申请人： American Express Travel Related Services Company, Inc.

发明人： Sushil S. Shimpi ,  Tyler C. Colton ,  Meera Girija Varma ,  Glenn S. Moriarty ,  Matthew B. Peters ,  Padma D. Jandhyala

IPC分类号： H04L67/50 ,  G06N5/04 ,  G08B21/18 ,  H04L51/212 ,  H04L67/306 ,  H04L67/55

CPC分类号： H04L67/535 ,  G06N5/04 ,  G08B21/182 ,  H04L51/212 ,  H04L67/306 ,  H04L67/55

摘要： Disclosed are various embodiments for a predictive communication system and related methods. One such method comprises training a predictive computer model to determine a ranking of intent insights based on prior servicing interactions with users; determining, from real-time activity data, that an accumulation of account activity data of the user has reached a specified threshold limit; generating, using the predictive computer model, a ranking of a plurality of intent insights associated with the communication; transmitting, to a client device of the user, the ranking of the plurality of intent insights and a user feedback inquiry comprising an accuracy inquiry to confirm whether individual ones of the plurality of intent insights are accurate; and retraining the predictive computer model of the at least one computing device based on the user feedback.

公开(公告)号：US12127007B2

公开(公告)日：2024-10-22

申请号：US18610658

申请日：2024-03-20

申请人： Digital Global Systems, Inc.

发明人： Armando Montalvo ,  Bryce Simmons

IPC分类号： H04L12/28 ,  G06F30/27 ,  G06N3/02 ,  G06N5/022 ,  G06N5/04 ,  G06N20/00 ,  G06N20/10 ,  G06N20/20 ,  H04L41/0893 ,  H04W16/10 ,  H04W24/02 ,  H04W72/0453 ,  G06N3/042 ,  G06N3/045 ,  H04J1/16 ,  H04L41/0894 ,  H04W16/14 ,  H04W24/08

CPC分类号： H04W16/10 ,  G06F30/27 ,  G06N3/02 ,  G06N5/022 ,  G06N5/04 ,  G06N20/00 ,  G06N20/10 ,  G06N20/20 ,  H04L41/0893 ,  H04W24/02 ,  H04W72/0453 ,  G06N3/042 ,  G06N3/045 ,  H04L41/0894 ,  H04W16/14 ,  H04W24/08

摘要： Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.

公开(公告)号：US12125146B1

公开(公告)日：2024-10-22

申请号：US17575091

申请日：2022-01-13

申请人： GridRaster, Inc.

发明人： Yiyong Tan ,  Bhaskar Banerjee ,  Rishi Ranjan

IPC分类号： G06T19/00 ,  G06F18/25 ,  G06N3/045 ,  G06N5/04 ,  G06N20/10

CPC分类号： G06T19/006 ,  G06F18/25 ,  G06N3/045 ,  G06N5/04 ,  G06N20/10

摘要： A mixed reality (MR) system and method performs three dimensional (3D) tracking using 3D deep neural network structures in which multimodal fusion and simplified machine learning to only cluster label distribution (output of 3D deep neural network trained by generic 3D benchmark dataset) is used to reduce the training data requirements of to directly train a 3D deep neural network structures for non-generic user case. In one embodiment, multiple 3D deep neural network structures, such as PointCNN, 3D-Bonet, RandLA, etc., may be trained by different generic 3D benchmark datasets, such as ScanNet, ShapeNet, S3DIS, inadequate 3D training dataset, etc.