公开(公告)号：US11836036B2

公开(公告)日：2023-12-05

申请号：US17439020

申请日：2020-03-09

Applicant: ABB Schweiz AG

Inventor： Abhilash Gopalakrishnan ,  Jithin Kizhakey Putanvetil ,  Manigandan P ,  Arinjai Gupta ,  Martin Nykvist

IPC: G06F11/00 ,  G06F11/07 ,  G06F11/30 ,  G06F18/2415

CPC classification number: G06F11/079 ,  G06F11/0772 ,  G06F11/3006 ,  G06F18/24155

Abstract: A method for detecting a fault in an intelligent electronic device that includes components uses a Bayesian network. The method includes detecting a failure event in the components, obtaining a first list of cause of failures in the component using a fault tree model, computing probability of the cause of failures to obtain a second list of probable causes of failure by monitoring of information about the elements identified in the first list, identifying a root cause of failure associated with the element comprised in the component using the Bayesian network based on the second list, and initiating a function. The function may be one of restarting the element having the root cause of failure, a filtering operation for input data provided to that element; and providing an alert in the human machine interface associated with the intelligent electronic device.

公开(公告)号：US20230377312A1

公开(公告)日：2023-11-23

申请号：US18125388

申请日：2023-03-23

Applicant: Veritone, Inc.

Inventor： Peter Nguyen ,  David Kettler ,  Karl Schwamb ,  Chad Steelberg

IPC: G06V10/764 ,  G10L15/16 ,  G10L15/04 ,  G10L15/22 ,  G10L15/06 ,  G06N3/08 ,  G06N3/04 ,  G10L25/78 ,  G10L15/32 ,  G10L15/02 ,  G06F18/21 ,  G06F18/20 ,  G06F18/2415 ,  G06N3/045 ,  G06N3/047

CPC classification number: G06V10/764 ,  G10L15/16 ,  G10L15/04 ,  G10L15/22 ,  G10L15/063 ,  G06N3/08 ,  G06N3/04 ,  G10L25/78 ,  G10L15/32 ,  G10L15/02 ,  G06F18/217 ,  G06F18/285 ,  G06F18/24155 ,  G06N3/045 ,  G06N3/047 ,  G06N5/01

Abstract: Methods and systems for training one or more neural networks for transcription and for transcribing a media file using the trained one or more neural networks are provided. One of the methods includes: segmenting the media file into a plurality of segments; inputting each segment, one segment at a time, of the plurality of segments into a first neural network trained to perform speech recognition; extracting outputs, one segment at a time, from one or more layers of the first neural network; and training a second neural network to generate a predicted-WER (word error rate) of a plurality of transcription engines for each segment based at least on outputs from the one or more layers of the first neural network.

公开(公告)号：US20230353461A1

公开(公告)日：2023-11-02

申请号：US18349637

申请日：2023-07-10

Applicant: Ribbon Communications Operating Company, Inc.

Inventor： Charles W. Boyle ,  Sreenivas NVR Kaki ,  Nizar K. Purayil ,  Vsevolod V. Ostapenko

IPC: H04L41/16 ,  G06F16/907 ,  G06N3/08 ,  G06Q10/0639 ,  G06N5/04 ,  H04L41/5009 ,  H04L43/0817 ,  H04L43/0823 ,  H04W24/02 ,  H04W24/04 ,  H04W24/08 ,  H04W24/10 ,  H04L65/1073 ,  H04M3/51 ,  H04L41/0631 ,  H04L65/65 ,  H04L65/1104 ,  G06F18/23 ,  G06F18/214 ,  G06F18/2415

CPC classification number: H04L41/16 ,  G06F16/907 ,  G06N3/08 ,  G06Q10/06393 ,  G06N5/04 ,  H04L41/5009 ,  H04L43/0817 ,  H04L43/0823 ,  H04W24/02 ,  H04W24/04 ,  H04W24/08 ,  H04W24/10 ,  H04L65/1073 ,  H04M3/5175 ,  H04L41/0631 ,  H04L65/65 ,  H04L65/1104 ,  G06F18/23 ,  G06F18/2148 ,  G06F18/24155

Abstract: A method performed by a computing system includes collecting information on transactions in a telecommunication system, using the information on transactions to create a plurality of event objects, each of the event objects associated with a telecommunication event, associating each of the event objects with a Key Performance Indicator (KPI), applying the event objects to a plurality of inference functions, each inference functions using the set of parameters as inputs and the KPIs of the event objects as outputs to create a model that infers a relationship between the set of parameters and the KPIs, and analyzing metadata from each of the inference functions to determine which of the set of parameters was used to predict an outcome leading to the KPI.

公开(公告)号：US11762753B2

公开(公告)日：2023-09-19

申请号：US17333209

申请日：2021-05-28

Applicant: GMECI, LLC

Inventor： Bradford R. Everman ,  Brian Scott Bradke

IPC: G06F11/34 ,  G06N20/00 ,  G06F18/214 ,  G06F18/2431 ,  G06F18/2415

CPC classification number: G06F11/3428 ,  G06F18/2155 ,  G06F18/2431 ,  G06F18/24155 ,  G06N20/00

Abstract: Aspects relate to system and methods for determining a user specific mission operational performance, using machine-learning processes. An exemplary system includes a computing device configured to perform operations including receiving user-input structured data from at least a user device, receiving observed structured data related to the user and a mission performance metric, inputting the user-input structured data and the observed structured data to a machine-learning model, generating a user performance metric as a function of the machine-learning model, receiving a deterministic mission operational performance metric, disaggregating a deterministic user performance metric as a function of the deterministic mission operation performance metric and the mission performance metric, inputting training data to a machine-learning algorithm, where the training data includes the user-input structured data and the observed structured data correlated to the deterministic user performance metric, and training the machine-learning model as a function of the machine-learning algorithm and the training data.

公开(公告)号：US11762346B2

公开(公告)日：2023-09-19

申请号：US17605485

申请日：2020-06-03

Applicant: Robert Bosch GmbH

Inventor： Edgar Klenske ,  Lukas Froehlich

IPC: G05B13/04 ,  G05B13/02 ,  G06F18/2415

CPC classification number: G05B13/042 ,  G05B13/0265 ,  G06F18/24155

Abstract: A computer-implemented method for creating a control process for a technical system using a Bayesian optimization method, the control process being created and executable based on model parameters of a control model, the following steps being performed in order to optimize the control process: furnishing a quality function that corresponds to a trainable regression function, and that assesses a quality of a control process of the technical system based on model parameters; executing a Bayesian optimization method based on the quality function in order to iteratively ascertain an optimized model parameter set having model parameters, such that during execution of the Bayesian optimization method, a model parameter domain that indicates the permissible value ranges for the model parameters is expanded, by an amount equal to an expansion distance, with respect to those dimensions for which the model parameter ascertained in the current iteration lies at a range boundary.

公开(公告)号：US11710065B2

公开(公告)日：2023-07-25

申请号：US16371460

申请日：2019-04-01

Applicant: Adobe Inc.

Inventor： Jun He ,  Shiyuan Gu ,  Zhenyu Yan ,  Wuyang Dai ,  Yi-Hong Kuo ,  Abhishek Pani

IPC: G06F40/279 ,  G06N20/00 ,  G06F18/2415 ,  G06F18/214 ,  G06N7/01

CPC classification number: G06N20/00 ,  G06F18/214 ,  G06F18/24155 ,  G06F40/279 ,  G06N7/01

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times to provide electronic communications based on predicted response rates by utilizing a Bayesian approach and multi-armed bandit algorithms. For example, the disclosed systems can generate predicted response rates by training and utilizing one or more response rate prediction models to generate a weighted combination of user-specific response information and population-specific response information. The disclosed systems can further utilize a Bayes upper-confidence-bound send time model to determine send times that are more likely to elicit user responses based on the predicted response rates and further based on exploration and exploitation considerations. In addition, the disclosed systems can update the response rate prediction models and/or the Bayes upper-confidence-bound send time model based on providing additional electronic communications and receiving additional responses to modify model weights.

公开(公告)号：US11688504B2

公开(公告)日：2023-06-27

申请号：US16699616

申请日：2019-11-30

Applicant: KPN Innovations, LLC

Inventor： Kenneth Neumann

IPC: G16H20/60 ,  G06N3/08 ,  G06V10/24 ,  G06N3/088 ,  G06F18/214 ,  G06F18/2413 ,  G06F18/2415

CPC classification number: G16H20/60 ,  G06F18/2155 ,  G06F18/24147 ,  G06F18/24155 ,  G06N3/088 ,  G06V10/245

Abstract: A system for informing food element decisions in the acquisition of edible materials from any source. The system includes a processor coupled to a memory configured to receive from a user client device a food element descriptor uniquely identifying a particular food element. The system retrieves from a physiological database at least an element of physiological data. The system identifies using at least an element of physiological data and a machine-learning algorithm user constitutional enhancing food elements and user constitutional advancing food elements. The system classifies using a food element classifier a food element descriptor. The system displays on a graphical user interface a constitutional enhancing food element or a constitutional advancing food element.

公开(公告)号：US20230177499A1

公开(公告)日：2023-06-08

申请号：US18101760

申请日：2023-01-26

Applicant: Walmart Apollo, LLC

Inventor： Souradip Chakraborty ,  Ojaswini Chhabra

IPC: G06Q20/38 ,  H04L9/32 ,  G06N3/08 ,  G06V40/30 ,  G06F18/2415 ,  G06V10/764 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82

CPC classification number: G06Q20/3825 ,  H04L9/3257 ,  G06N3/08 ,  G06V40/33 ,  G06F18/24155 ,  G06V10/764 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82

Abstract: Two models are first trained and then test images are applied to the two trained models in an effort to detect signature forgeries. The first model is trained with pairs of signature images and the resultant trained model is capable of detecting blind forgeries. The second model is trained with triplets of signature images and is capable of detecting skilled signature forgeries. After the two models are trained, test images are applied to the models and determinations are made as to whether a blind or skilled forgery is present.

公开(公告)号：US11669588B2

公开(公告)日：2023-06-06

申请号：US17823257

申请日：2022-08-30

Applicant: Oxylabs, UAB

Inventor： Martynas Juravicius ,  Andrius Kuksta

IPC: G06F18/214 ,  G06N5/025 ,  G06N20/00 ,  G06F18/2411 ,  G06F18/2415 ,  G06F18/243 ,  G06N3/044 ,  G06F16/951 ,  G06F21/57 ,  H04L9/40 ,  H04L67/02

CPC classification number: G06F18/214 ,  G06F18/2411 ,  G06F18/24155 ,  G06F18/24323 ,  G06N3/044 ,  G06N5/025 ,  G06N20/00 ,  G06F16/951 ,  G06F21/577 ,  G06F2216/03 ,  H04L63/1433 ,  H04L67/02

Abstract: Systems and methods that allow examination of response data collected from content providers and provide for classification and routing according to the classification. The process of classification employs an unsupervised, or partially unsupervised, Machine Learning classifier model for identifying data collection responses that contains no data, mangled data, or a block, for assigning a classification correspondingly and for feeding the classification decision back to a data collection platform.

公开(公告)号：US20230141553A1

公开(公告)日：2023-05-11

申请号：US17945876

申请日：2022-09-15

Applicant: Cerebri AI Inc.

Inventor： Jean Belanger ,  Michael L. Roberts ,  Gabriel M. Silberman ,  Karen Bennet

IPC: G06Q30/0201 ,  G06Q10/067 ,  G06N20/00 ,  G06F18/2321 ,  G06F18/2415 ,  G06N7/01 ,  G06N5/025

CPC classification number: G06Q30/0201 ,  G06Q10/067 ,  G06N20/00 ,  G06F18/2321 ,  G06F18/24155 ,  G06N7/01 ,  G06N5/025 ,  G06F8/35

Abstract: In some implementations, an event timeline that includes one or more interactions between a customer and a supplier may be determined. A starting value may be assigned to individual events in the event timeline. A sub-sequence comprising a portion of the event timeline that includes at least one reference event may be selected. A classifier may be used to determine a previous relative value for a previous event that occurred before the reference event and to determine a next relative value for a next event that occurred after the reference event until all events in the event timeline have been processed. The events in the event timeline may be traversed and a monetized value index assigned to individual events in the event timeline.