公开(公告)号：US12019703B2

公开(公告)日：2024-06-25

申请号：US17838776

申请日：2022-06-13

Applicant: TripleBlind, Inc.

Inventor： Greg Storm ,  Riddhiman Das ,  Babak Poorebrahim Gilkalaye

IPC: G06F17/16 ,  G06F18/2113 ,  G06N3/04 ,  G06N3/045 ,  G06N3/048 ,  G06Q20/12 ,  G06Q20/40 ,  G06Q30/0601 ,  G06V10/82 ,  H04L9/06 ,  H04L9/40 ,  G06F18/24 ,  G06F18/2413 ,  G06N3/082 ,  G06N3/084 ,  G06V10/44 ,  G06V10/764 ,  H04L9/00 ,  H04L9/08

CPC classification number: G06F17/16 ,  G06N3/045 ,  G06N3/048 ,  G06Q30/0623 ,  H04L9/0625 ,  H04L63/0428 ,  G06F18/2113 ,  G06F18/24 ,  G06N3/04 ,  G06N3/082 ,  G06N3/084 ,  G06Q20/401 ,  G06Q2220/00 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  H04L9/008 ,  H04L9/085 ,  H04L2209/46

Abstract: A method includes receiving, on a computer-implemented system and from user, an identification of data and an identification of an algorithm and, based on a user interaction with the computer-implemented system comprising a one-click interaction or a two-click interaction. Without further user input, the method includes dividing the data into a data first subset and a data second subset, dividing the algorithm (or a Boolean logic gate representation of the algorithm) into an algorithm first subset and an algorithm second subset, running, on the computer-implemented system at a first location, the data first subset with the algorithm first subset to yield a first partial result, running, on the computer-implemented system at a second location separate from the first location, the data second subset with the algorithm second subset to yield a second partial result and outputting a combined result based on the first partial result and the second partial result.

公开(公告)号：US20240203568A1

公开(公告)日：2024-06-20

申请号：US18594634

申请日：2024-03-04

Applicant: PROSCIA INC.

Inventor： Brian H. Jackson ,  Coleman C. Stavish

IPC: G16H30/40 ,  G06F18/2413 ,  G06N3/08 ,  G06T7/00 ,  G06V10/44 ,  G06V10/764 ,  G06V20/69

CPC classification number: G16H30/40 ,  G06F18/2413 ,  G06N3/08 ,  G06T7/0014 ,  G06V10/454 ,  G06V10/764 ,  G06V20/695 ,  G06V20/698 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06T2207/30088

Abstract: Techniques for classifying a human cutaneous tissue specimen are presented. The techniques may include obtaining a computer readable image of the human tissue sample and preprocessing the image. The techniques may include applying a trained deep learning model to the image to label each of a plurality of image pixels with at least one probability representing a particular diagnosis, such that a labeled plurality of image pixels is obtained. The techniques can also include applying a trained discriminative classifier to contiguous regions of pixels defined at least in part by the labeled plurality of image pixels to obtain a specimen level diagnosis, where the specimen level diagnosis includes at least one of: basal cell carcinoma, dermal nevus, or seborrheic keratosis. The techniques can include outputting the specimen level diagnosis.

公开(公告)号：US20240192320A1

公开(公告)日：2024-06-13

申请号：US18582358

申请日：2024-02-20

Applicant: NVIDIA Corporation

Inventor： Tommi Koivisto ,  Pekka Janis ,  Tero Kuosmanen ,  Timo Roman ,  Sriya Sarathy ,  William Zhang ,  Nizar Assaf ,  Colin Tracey

IPC: G01S7/41 ,  B60W50/00 ,  G01S7/48 ,  G01S13/86 ,  G01S13/931 ,  G01S17/931 ,  G06F16/35 ,  G06F18/21 ,  G06F18/214 ,  G06F18/23 ,  G06F18/2413 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/048 ,  G06N3/084 ,  G06N20/00 ,  G06V10/20 ,  G06V10/44 ,  G06V10/46 ,  G06V10/762 ,  G06V10/764 ,  G06V10/77 ,  G06V10/774 ,  G06V20/58

CPC classification number: G01S7/417 ,  B60W50/00 ,  G06F16/35 ,  G06F18/214 ,  G06F18/217 ,  G06F18/23 ,  G06F18/2414 ,  G06N3/044 ,  G06N3/045 ,  G06N3/084 ,  G06N20/00 ,  G06V10/255 ,  G06V10/454 ,  G06V10/46 ,  G06V10/762 ,  G06V10/764 ,  G06V10/7715 ,  G06V10/774 ,  G06V20/58 ,  G06V20/584 ,  G01S7/412 ,  G01S7/4802 ,  G01S13/867 ,  G01S2013/9318 ,  G01S2013/9323 ,  G01S17/931 ,  G06N3/047 ,  G06N3/048

Abstract: In various examples, detected object data representative of locations of detected objects in a field of view may be determined. One or more clusters of the detected objects may be generated based at least in part on the locations and features of the cluster may be determined for use as inputs to a machine learning model(s). A confidence score, computed by the machine learning model(s) based at least in part on the inputs, may be received, where the confidence score may be representative of a probability that the cluster corresponds to an object depicted at least partially in the field of view. Further examples provide approaches for determining ground truth data for training object detectors, such as for determining coverage values for ground truth objects using associated shapes, and for determining soft coverage values for ground truth objects.

公开(公告)号：US12008758B2

公开(公告)日：2024-06-11

申请号：US17952234

申请日：2022-09-24

Applicant: PSIP LLC

Inventor： Salmaan Hameed ,  Giau Nguyen

IPC: G06T7/00 ,  A61B1/00 ,  G01B11/03 ,  G01B11/30 ,  G06F18/2413 ,  G06V10/44 ,  G06V10/50 ,  G06V10/764 ,  G06V10/82

CPC classification number: G06T7/0014 ,  A61B1/000094 ,  A61B1/000096 ,  A61B1/0014 ,  A61B1/00177 ,  A61B1/00181 ,  G01B11/03 ,  G01B11/30 ,  G06V10/454 ,  G06V10/50 ,  G06V10/764 ,  G06V10/82 ,  A61B1/00101 ,  G06F18/2414 ,  G06T2207/10068 ,  G06T2207/30032 ,  G06V2201/032 ,  G06V2201/034

Abstract: Identifying polyps or lesions in a colon. In some variations, computer-implemented methods for polyp detection may be used in conjunction with an endoscope system to analyze the images captured by the endoscopic system, identify any polyps and/or lesions in a visual scene captured by the endoscopic system, and provide an indication to the practitioner that a polyp and/or lesion has been detected.

公开(公告)号：US12008074B2

公开(公告)日：2024-06-11

申请号：US17622935

申请日：2019-07-05

Applicant: NEC Corporation

Inventor： Soma Shiraishi

IPC: G06F18/21 ,  G06F18/214 ,  G06F18/2413

CPC classification number: G06F18/214 ,  G06F18/2413

Abstract: A learning device is configured to acquire first training data that is training data regarding a registered category object and second training data regarding an unregistered category object, the registered category object belonging to a registered category which is registered as an identification target, the unregistered category object not belonging to the registered category. The learning device is also configured to calculate, on a basis of the first training data and the second training data, an identification score indicating a degree of certainty that an object to be identified belongs to the registered category. The learning device is also configured to calculate, on a basis of the second training data, an unregistered score indicating a degree of certainty that the object to be identified does not belong to the registered category. The learning device is also configured to learn an identification model which performs an identification regarding the registered category based on the identification score and the unregistered score.

公开(公告)号：US12002096B1

公开(公告)日：2024-06-04

申请号：US17883351

申请日：2022-08-08

Applicant: MASSACHUSETTS MUTUAL LIFE INSURANCE COMPANY

Inventor： David Lipke ,  Nailong Zhang

IPC: G06Q40/06 ,  G06F16/951 ,  G06F17/18 ,  G06F18/214 ,  G06F18/2413

CPC classification number: G06Q40/06 ,  G06F16/951 ,  G06F17/18 ,  G06F18/2148 ,  G06F18/24147

Abstract: Disclosed are method and systems to program a server to identify the value of a fund comprising shares of multiple private entities. The server receives transaction data associated with a fund where the transaction data identifies a proportion of shares within the fund associated with each private entity, price per share of each private entity, and other relevant data. The server then executes multiple artificial intelligence models to identify comparable public entities to each private entity. The server then retrieves stock price data for each public entity and calculates a value for each private entity in real time. The server also displays a value of the fund in real time where identification of each private entity is anonymized.

公开(公告)号：US11995763B2

公开(公告)日：2024-05-28

申请号：US17257012

申请日：2019-07-02

Applicant: VAYYAR IMAGING LTD.

Inventor： Shay Moshe ,  Ian Podkamien

IPC: G06T17/00 ,  G01S13/89 ,  G06F18/2413 ,  G06F18/2431 ,  G06T15/04 ,  G06V20/52 ,  G06V20/64

CPC classification number: G06T17/00 ,  G01S13/89 ,  G06F18/2413 ,  G06F18/2431 ,  G06T15/04 ,  G06V20/52 ,  G06V20/64

Abstract: Systems and methods thereof configured for mapping an environment, comprising: continuously scanning, via at least one transmit/receive module, the environments volume for detecting objects, the transmit/receive module is configured to transmit signals and receive their reflection; measuring the location of the objects' reflected signals and optionally their strength, via at least one acquisition module and at least one processor; and constructing a real-time updated three-dimensional (3D) voxels' map of the environment; and associating for each voxel a time record of events and optionally their respective event features; wherein each event comprises at least the voxel's detected presence of signal/s.

公开(公告)号：US20240169554A1

公开(公告)日：2024-05-23

申请号：US18425629

申请日：2024-01-29

Applicant: Stats LLC

Inventor： Long Sha ,  Sujoy Ganguly ,  Xinyu Wei ,  Patrick Joseph Lucey ,  Aditya Cherukumudi

IPC: G06T7/20 ,  G06F18/2135 ,  G06F18/214 ,  G06F18/22 ,  G06F18/232 ,  G06F18/2413 ,  G06N3/08 ,  G06T7/00 ,  G06T7/70 ,  G06T7/73 ,  G06T7/80 ,  G06V10/44 ,  G06V10/764 ,  G06V10/82 ,  G06V20/40 ,  G06V40/20 ,  H04N21/44

CPC classification number: G06T7/20 ,  G06F18/2135 ,  G06F18/214 ,  G06F18/22 ,  G06F18/232 ,  G06F18/2413 ,  G06N3/08 ,  G06T7/70 ,  G06T7/73 ,  G06T7/80 ,  G06T7/97 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  G06V20/42 ,  G06V20/46 ,  G06V20/48 ,  G06V20/49 ,  G06V40/20 ,  H04N21/44008 ,  G06T2207/10016 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30221 ,  G06T2207/30244 ,  G06V20/44

Abstract: A system and method of calibrating moving cameras capturing a sporting event is disclosed herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system labels, via a neural network, components of a playing surface captured in each video frame. The computing system matches a subset of labeled video frames to a set of templates with various camera perspectives. The computing system fits a playing surface model to the set of labeled video frames that were matched to the set of templates. The computing system identifies camera motion in each video frame using an optical flow model. The computing system generates a homography matrix for each video frame based on the fitted playing surface model and camera motion. The computing system calibrates each camera based on the homography matrix generated for each video frame.

公开(公告)号：US20240160937A1

公开(公告)日：2024-05-16

申请号：US18418197

申请日：2024-01-19

Applicant: Google LLC

Inventor： Rui Zhang ,  Jia Li ,  Tomas Jon Pfister

IPC: G06N3/084 ,  G06F18/21 ,  G06F18/2134 ,  G06F18/214 ,  G06F18/22 ,  G06F18/2413 ,  G06N3/045 ,  G06N3/047 ,  G06V10/74 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82

CPC classification number: G06N3/084 ,  G06F18/21347 ,  G06F18/214 ,  G06F18/2148 ,  G06F18/2193 ,  G06F18/22 ,  G06F18/2413 ,  G06N3/045 ,  G06N3/047 ,  G06V10/761 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82

Abstract: A method includes obtaining a source training dataset that includes a plurality of source training images and obtaining a target training dataset that includes a plurality of target training images. For each source training image, the method includes translating, using the forward generator neural network G, the source training image to a respective translated target image according to current values of forward generator parameters. For each target training image, the method includes translating, using a backward generator neural network F, the target training image to a respective translated source image according to current values of backward generator parameters. The method also includes training the forward generator neural network G jointly with the backward generator neural network F by adjusting the current values of the forward generator parameters and the backward generator parameters to optimize an objective function.

公开(公告)号：US11983950B2

公开(公告)日：2024-05-14

申请号：US18172141

申请日：2023-02-21

Applicant: X Development LLC

Inventor： Barnaby John James ,  Grace Taixi Brentano ,  Christopher Thornton

IPC: G06V40/10 ,  A01K61/95 ,  A01K63/02 ,  F24F11/30 ,  G06F18/213 ,  G06F18/214 ,  G06F18/22 ,  G06F18/2413 ,  G06N20/00 ,  G06T3/40 ,  G06V10/24 ,  G06V10/25 ,  G06V10/40 ,  G06V10/44 ,  G06V10/762 ,  G06V10/764 ,  G06V10/77 ,  G06V20/52 ,  G06V20/80

CPC classification number: G06V40/10 ,  A01K61/95 ,  A01K63/02 ,  F24F11/30 ,  G06F18/213 ,  G06F18/214 ,  G06F18/22 ,  G06F18/2413 ,  G06N20/00 ,  G06T3/40 ,  G06V10/245 ,  G06V10/25 ,  G06V10/40 ,  G06V10/44 ,  G06V10/762 ,  G06V10/764 ,  G06V10/7715 ,  G06V20/52 ,  G06V20/80 ,  F24F2221/225

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for identification and re-identification of fish. In some implementations, first media representative of aquatic cargo is received. Second media based on the first media is generated, wherein a resolution of the second media is higher than a resolution of the first media. A cropped representation of the second media is generated. The cropped representation is provided to the machine learning model. In response to providing the cropped representation to the machine learning model, an embedding representing the cropped representation is generated using the machine learning model. The embedding is mapped to a high dimensional space. Data identifying the aquatic cargo is provided to a database, wherein the data identifying the aquatic cargo comprises an identifier of the aquatic cargo, the embedding, and a mapped region of the high dimensional space.