公开(公告)号：US12106221B2

公开(公告)日：2024-10-01

申请号：US16440862

申请日：2019-06-13

Applicant: International Business Machines Corporation

Inventor： Peifeng Yin ,  Yunyao Li ,  Taiga Nakamura

IPC: G06N3/084 ,  G06F18/2113 ,  G06F18/214 ,  G06F18/243 ,  G06N20/00

CPC classification number: G06N3/084 ,  G06F18/2113 ,  G06F18/2155 ,  G06F18/24323 ,  G06N20/00

Abstract: A computer-implemented method according to one embodiment includes receiving, at a scheduler, a training data instance and a target instance, generating, by the scheduler, an input sequence from the training data instance and the target instance, sending the input sequence from the scheduler to an encoder, mapping, by the encoder, the input sequence to a feature vector, sending the feature vector from the encoder to the scheduler, sending the feature vector from the scheduler to a predictor, and mapping, by the predictor, the feature vector to a class vector to create a label for the target instance.

公开(公告)号：US12096733B2

公开(公告)日：2024-09-24

申请号：US18114681

申请日：2023-02-27

Applicant: DOGTOOTH TECHNOLOGIES LIMITED

Inventor： Duncan Robertson ,  Matthew Cook ,  Edward Herbert ,  Frank Tully

IPC: A01G9/14 ,  A01D46/22 ,  A01D46/24 ,  A01D46/253 ,  A01D46/28 ,  A01D46/30 ,  B25J9/16 ,  B25J11/00 ,  B25J15/00 ,  G06F18/214 ,  G06F18/24 ,  G06F18/243 ,  G06Q30/0283 ,  G06T7/00 ,  G06T7/11 ,  G06T7/50 ,  G06T7/60 ,  G06T7/70 ,  G06T7/90 ,  G06V20/10 ,  B25J9/00 ,  B25J9/06 ,  G05D1/00 ,  G06V20/68

CPC classification number: A01G9/143 ,  A01D46/22 ,  A01D46/243 ,  A01D46/253 ,  A01D46/28 ,  A01D46/30 ,  B25J9/1679 ,  B25J9/1697 ,  B25J11/00 ,  B25J15/0019 ,  G06F18/2148 ,  G06F18/24323 ,  G06F18/24765 ,  G06Q30/0283 ,  G06T7/0004 ,  G06T7/11 ,  G06T7/50 ,  G06T7/60 ,  G06T7/70 ,  G06T7/90 ,  G06V20/10 ,  B25J9/0084 ,  B25J9/06 ,  B25J15/0033 ,  G05B2219/45003 ,  G05D1/0219 ,  G06T2207/10048 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30128 ,  G06V20/68 ,  Y02A40/25

Abstract: A robotic fruit picking system includes an autonomous robot that includes a positioning subsystem that enables autonomous positioning of the robot using a computer vision guidance system. The robot also includes at least one picking arm and at least one picking head, or other type of end effector, mounted on each picking arm to either cut a stem or branch for a specific fruit or bunch of fruits or pluck that fruit or bunch. A computer vision subsystem analyses images of the fruit to be picked or stored and a control subsystem is programmed with or learns picking strategies using machine learning techniques. A quality control (QC) subsystem monitors the quality of fruit and grades that fruit according to size and/or quality. The robot has a storage subsystem for storing fruit in containers for storage or transportation, or in punnets for retail.

公开(公告)号：US20240311354A1

公开(公告)日：2024-09-19

申请号：US18673776

申请日：2024-05-24

Applicant: Capital One Services, LLC

Inventor： Srinivasarao Daruna ,  Vijay Sahebgouda Bantanur ,  Marisa Lee

IPC: G06F16/215 ,  G06F3/0481 ,  G06F16/23 ,  G06F18/22 ,  G06F18/243 ,  G06N20/00

CPC classification number: G06F16/215 ,  G06F3/0481 ,  G06F16/2322 ,  G06F18/22 ,  G06F18/24323 ,  G06N20/00

Abstract: Systems and methods of the present disclosure enable a processor to automatically detect duplicate data entries by receiving data entries associated with a user, where each data entry includes a value, a time, an entity identifier, and a location. Pairs of similar data entries are determined by matching the entity identifier and the location pairs data entries. Candidate duplicate data entries are determined based on a proximity in time between data entries of the similar data entries. For each candidate duplicate data entry, a feature vector is generated including the entity identifier, location, value and time, and each feature vector is submitted to a duplicate classification model to automatically determine duplicate data entries from the candidate duplicate data entries, the duplicate classification model being trained according to a historical dispute entries.

公开(公告)号：US12067219B2

公开(公告)日：2024-08-20

申请号：US18060717

申请日：2022-12-01

Applicant: Truist Bank

Inventor： Alexis Pastore

IPC: G06F3/0484 ,  G06F3/0482 ,  G06Q20/22 ,  G06Q20/24 ,  G06Q40/03 ,  G06F18/21 ,  G06F18/2321 ,  G06F18/2413 ,  G06F18/243 ,  G06N3/08 ,  G06N20/00

CPC classification number: G06F3/0484 ,  G06F3/0482 ,  G06Q20/227 ,  G06Q20/24 ,  G06Q40/03 ,  G06F18/217 ,  G06F18/2321 ,  G06F18/24143 ,  G06F18/24323 ,  G06N3/08 ,  G06N20/00

Abstract: A system and method for allowing a user to manage transactions in an online 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 a plurality of external bank accounts to the online application, provide a main list of transactions that were made using a credit card, allow a user to selectively move the transactions from the main list to a first list or a second list, and allow the user to pay the transactions in the first list from one of the external bank accounts and pay the transactions in the second list from another one of the external bank accounts.

公开(公告)号：US12067052B2

公开(公告)日：2024-08-20

申请号：US17133330

申请日：2020-12-23

Applicant: AlgoLook, Inc.

Inventor： David E. Harrison ,  Jeremy C. Patton ,  John Dankovchik

IPC: G06F16/732 ,  G05B13/02 ,  G06F18/243 ,  G06N3/08 ,  G06N5/04 ,  G06N20/00 ,  G06T5/50 ,  G06V10/96 ,  G06V20/40 ,  G06V20/52 ,  G06V20/64 ,  G06V40/16 ,  H04N5/33 ,  H04N23/611 ,  F24F11/63 ,  G01J5/00 ,  G01J5/48 ,  G06F16/25

CPC classification number: G06F16/7335 ,  G05B13/0265 ,  G06F18/24323 ,  G06N3/08 ,  G06N5/04 ,  G06N20/00 ,  G06T5/50 ,  G06V10/96 ,  G06V20/41 ,  G06V20/46 ,  G06V20/52 ,  G06V20/64 ,  G06V40/166 ,  G06V40/172 ,  H04N5/33 ,  H04N23/611 ,  F24F11/63 ,  G01J2005/0077 ,  G01J5/48 ,  G06F16/252 ,  G06T2207/10028 ,  G06T2207/30201 ,  G06V20/44

Abstract: A method classifies air particulate data. Air sensor data is received from an air sensor. The air sensor data comprises a particulate vector. A machine learning model generates output from the air sensor data using a machine learning model. A notification that uses the machine learning model output is transmitted by a control system.

公开(公告)号：US20240252121A1

公开(公告)日：2024-08-01

申请号：US18634281

申请日：2024-04-12

Applicant: Whoop, Inc.

Inventor： John Vincenzo Capodilupo ,  Behnoosh Tavakoli ,  Mostafa Ghannad-Rezaie

IPC: A61B5/00 ,  A61B5/0205 ,  A61B5/024 ,  A61B5/0245 ,  A61B5/0533 ,  A61B5/08 ,  A61B5/11 ,  A61B5/145 ,  G06F18/243

CPC classification number: A61B5/7267 ,  A61B5/02055 ,  A61B5/02416 ,  A61B5/02438 ,  A61B5/681 ,  A61B5/7221 ,  G06F18/24323 ,  A61B5/0022 ,  A61B5/02405 ,  A61B5/0245 ,  A61B5/0533 ,  A61B5/0816 ,  A61B5/1118 ,  A61B5/14542 ,  A61B5/4812 ,  A61B5/4815 ,  A61B5/4866 ,  A61B5/6831 ,  A61B2560/0209 ,  A61B2560/0223 ,  A61B2560/0242 ,  A61B2562/0219

Abstract: Sleep need for a user is assessed using continuous physiological data from a wearable monitor. In particular, by calculating a first sleep debt metric based on user strain and a second sleep debt metric based on accumulated sleep debt, an objective metric can be obtained that estimates an amount of sleep needed by the user in a next sleep period. This approach takes advantage of multiple modes of information embedded in the physiological data, such as a sleep and exercise patterns for a user over one or more preceding days.

公开(公告)号：US20240233923A1

公开(公告)日：2024-07-11

申请号：US18614524

申请日：2024-03-22

Applicant: Align Technology, Inc.

Inventor： Ya XUE ,  Jeeyoung CHOI ,  Justin B. MOORE ,  Anton SPIRIDONOV

IPC: G16H30/40 ,  A61C7/00 ,  A61C7/08 ,  A61C9/00 ,  G06F18/21 ,  G06F18/2413 ,  G06F18/243 ,  G06N3/08 ,  G06T7/10 ,  G06T7/66 ,  G16H50/20 ,  G16H50/50

CPC classification number: G16H30/40 ,  A61C7/002 ,  A61C7/08 ,  A61C9/0053 ,  G06F18/21 ,  G06F18/24147 ,  G06F18/24323 ,  G06T7/10 ,  G06T7/66 ,  G06N3/08 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20164 ,  G06T2207/30036 ,  G16H50/20 ,  G16H50/50

Abstract: Methods and systems for automatically determining an eruption status and/or primary or permanent tooth type of a target tooth. Methods may include determining tooth shape features of the target tooth from a 3D model of the patient's teeth. The methods may also include normalizing at least some of the tooth shape features using the tooth shape features of one or more reference teeth. The normalized tooth shape features may be applied to a classifier. Applying the normalized tooth shape features to the classifier may include applying either a first level binary classifier or a first level binary classifier and a second level binary classifier to the normalized tooth shape features.

公开(公告)号：US11983249B2

公开(公告)日：2024-05-14

申请号：US17593398

申请日：2020-03-24

Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

Inventor： Hidetoshi Kawaguchi

IPC: G06F18/25 ,  G06F18/2411 ,  G06F18/243 ,  G06N20/00

CPC classification number: G06F18/24323 ,  G06F18/2411 ,  G06F18/254 ,  G06N20/00

Abstract: An error determination device includes a class estimation process observation unit configured to acquire data in a process of being estimated, from a class estimation unit that estimates a class of data to be classified and generate an estimation process feature vector based on the acquired data; and an error determination unit configured to accept input of the estimation process feature vector generated by the class estimation process observation unit and a classification result output from the class estimation unit and determine whether the classification result is correct or incorrect based on the estimation process feature vector and the classification result, wherein the error determination unit is a functional part generated by machine learning based on an estimation process feature vector list created by adding a pseudo feature vector to an estimation process feature vector list generated by the class estimation process observation unit and on a learning error-correction list indicating that a class corresponding to the pseudo feature vector is incorrect.

公开(公告)号：US11977720B1

公开(公告)日：2024-05-07

申请号：US18155826

申请日：2023-01-18

Applicant: Truist Bank

Inventor： Alexis Pastore

IPC: G06F3/0484 ,  G06F3/0482 ,  G06F18/21 ,  G06F18/2321 ,  G06F18/2413 ,  G06F18/243 ,  G06N3/08 ,  G06N20/00 ,  G06Q20/24 ,  G06Q40/03

CPC classification number: G06F3/0484 ,  G06F3/0482 ,  G06Q20/24 ,  G06Q40/03 ,  G06F18/217 ,  G06F18/2321 ,  G06F18/24143 ,  G06F18/24323 ,  G06N3/08 ,  G06N20/00

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.

公开(公告)号：US20240118988A1

公开(公告)日：2024-04-11

申请号：US18529639

申请日：2023-12-05

Applicant: Microsoft Technology Licensing, LLC

Inventor： Connie Qin YANG ,  Matthew Scott ROSOFF ,  Nithin ADAPA ,  Logan RINGER ,  Steve Ku LIM ,  Xiaoyu CHAI

IPC: G06F11/34 ,  G06F11/30 ,  G06F11/32 ,  G06F18/243

CPC classification number: G06F11/3452 ,  G06F11/302 ,  G06F11/327 ,  G06F11/328 ,  G06F11/3466 ,  G06F18/24323

Abstract: Systems and methods directed to generating a predicted quality metric are provided. Telemetry data may be received from a from a first group of devices executing first software. A quality metric for the first software may be generated based on the first telemetry data. Telemetry data from a second group of devices may be received, where the second group of devices is different from the first group of devices. Covariates impacting the quality metric based on features included in the first telemetry data and the second telemetry data may be identified, and a coarsened exact matching process may be performed utilizing the identified covariates to generate a predicted quality metric for the first software based on the second group of devices.