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公开(公告)号:US20240311426A1
公开(公告)日:2024-09-19
申请号:US18677371
申请日:2024-05-29
申请人: GRACENOTE, INC.
IPC分类号: G06F16/901 , G06F16/65 , G06F16/683 , G06F18/2115
CPC分类号: G06F16/9014 , G06F16/65 , G06F16/683 , G06F18/2115 , G06F2218/16
摘要: Methods, apparatus, systems, and articles of manufacture are disclosed to improve media identification. An example apparatus includes a hash handler to generate a first set of reference matches by performing hash functions on a subset of media data associated with media to generate hashed media data based on a first bucket size, a candidate determiner to identify a second set of reference matches that include ones of the first set, the second set including ones having first quantities of hits that did not satisfy a threshold, determine second quantities of hits for ones of the second set by matching ones to the hash tables based on a second bucket size, and identify one or more candidate matches based on at least one of (1) ones of the first set or (2) ones of the second set, and a report generator to generate a report including a media identification.
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2.
公开(公告)号:US20240212848A1
公开(公告)日:2024-06-27
申请号:US18523660
申请日:2023-11-29
申请人: GRAIL, LLC
发明人: M. Cyrus MAHER
IPC分类号: G16H50/20 , C12Q1/6886 , G06F18/2115 , G16H50/30 , G16H50/70
CPC分类号: G16H50/20 , G06F18/2115 , G16H50/30 , G16H50/70 , C12Q1/6886
摘要: Systems and methods for classifier training are provided. A first dataset is obtained that comprises, for each first subject, a corresponding plurality of bin values, each for a bin in a plurality of bins, and subject cancer condition. A feature extraction technique is applied to the first dataset thereby obtaining feature extraction functions, each of which is an independent linear or nonlinear function of bin values of the bins. A second dataset is obtained comprising, for each second subject, a corresponding plurality of bin values, each for a bin in the plurality of bins and subject cancer condition. The plurality of bin values of each corresponding subject in the second plurality are projected onto the respective feature extraction functions, thereby forming a transformed second dataset comprising feature values for each subject. The transformed second dataset and subject cancer condition serves to train a classifier on the cancer condition set.
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3.
公开(公告)号:US12001931B2
公开(公告)日:2024-06-04
申请号:US16219242
申请日:2018-12-13
发明人: Ousef Kuruvilla
IPC分类号: G06N20/20 , G06F18/2115 , G06F18/214 , G06N3/126 , G06N20/00
CPC分类号: G06N20/20 , G06F18/2115 , G06F18/214 , G06N3/126 , G06N20/00
摘要: Aspects relate to a machine learning system implementing an evolutionary boosting machine. The system may initially select randomized feature sets for an initial generation of candidate models. Evolutionary algorithms may be applied to the system to create later generations of the cycle, combining and mutating the feature selections of the candidate models. The system may determine optimal number of boosting iterations for each candidate model in a generation by building boosting iterations from an initial value up to a predetermined maximum number of boosting iterations. When a final generation is achieved, the system may evaluate the optimal model of the generation. If the optimal boosting iterations of the optimal model does not meet solution constraints on the optimal boosting iterations, the system may adjust a learning rate parameter and then proceed to the next cycle. Based on termination criteria, the system may determine a resulting/final optimal mode.
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公开(公告)号:US20240161035A1
公开(公告)日:2024-05-16
申请号:US18087086
申请日:2022-12-22
申请人: Enlitic, Inc.
发明人: Kevin Lyman , Anthony Upton , Jordan Francis , Vicky Li , Mark Freudenberg , Alexander Pong , Alexander Freska , Zachary Holt
IPC分类号: G06Q10/0631 , A61B5/00 , G06F3/0482 , G06F3/0484 , G06F9/54 , G06F16/245 , G06F18/21 , G06F18/2115 , G06F18/214 , G06F18/2415 , G06F18/40 , G06F21/62 , G06N5/04 , G06N5/045 , G06N20/00 , G06N20/20 , G06Q20/14 , G06T3/40 , G06T5/50 , G06T5/70 , G06T5/94 , G06T7/00 , G06T7/10 , G06T7/11 , G06T7/187 , G06T7/44 , G06T11/00 , G06T11/20 , G06V10/22 , G06V10/25 , G06V10/764 , G06V10/82 , G06V30/19 , G06V40/16 , G16H10/20 , G16H10/60 , G16H15/00 , G16H30/20 , G16H30/40 , G16H40/20 , G16H50/20 , H04L67/01 , H04L67/12
CPC分类号: G06Q10/06315 , A61B5/7264 , G06F3/0482 , G06F3/0484 , G06F9/542 , G06F16/245 , G06F18/2115 , G06F18/214 , G06F18/217 , G06F18/2415 , G06F18/41 , G06F21/6254 , G06N5/04 , G06N5/045 , G06N20/00 , G06N20/20 , G06Q20/14 , G06T3/40 , G06T5/50 , G06T5/70 , G06T5/94 , G06T7/0012 , G06T7/0014 , G06T7/10 , G06T7/11 , G06T7/187 , G06T7/44 , G06T7/97 , G06T11/001 , G06T11/006 , G06T11/206 , G06V10/225 , G06V10/25 , G06V10/764 , G06V10/82 , G06V30/19173 , G06V40/171 , G16H10/20 , G16H10/60 , G16H15/00 , G16H30/20 , G16H30/40 , G16H40/20 , G16H50/20 , H04L67/01 , H04L67/12 , G16H50/70
摘要: A medical scan viewing system is configured to: generate inference data via at least one inference function, based the at least one medical scan and further based on receiver operating characteristic (ROC) parameters that include at least one ROC set point; present for display, via an interactive user interface, medical image data corresponding to the at least one medical scan, the inference data and a ROC adjustment tool; generate, in response to user interaction with the ROC adjustment tool, at least one adjusted ROC set point; generate updated inference data via the at least one inference function, based the at least one medical scan and further based on the at least one adjusted ROC set point; and present for display, via the interactive user interface, the medical image data corresponding to the at least one medical scan and the updated inference data.
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5.
公开(公告)号:US11921815B2
公开(公告)日:2024-03-05
申请号:US17019258
申请日:2020-09-13
发明人: Alberto Polleri , Sergio Aldea Lopez , Marc Michiel Bron , Dan David Golding , Alexander Ioannides , Maria del Rosario Mestre , Hugo Alexandre Pereira Monteiro , Oleg Gennadievich Shevelev , Larissa Cristina Dos Santos Romualdo Suzuki , Xiaoxue Zhao , Matthew Charles Rowe
IPC分类号: G06F18/213 , G06F8/75 , G06F8/77 , G06F11/30 , G06F11/34 , G06F16/21 , G06F16/23 , G06F16/2457 , G06F16/28 , G06F16/36 , G06F16/901 , G06F16/9035 , G06F16/907 , G06F18/10 , G06F18/2115 , G06F18/214 , G06N5/01 , G06N5/025 , G06N20/00 , G06N20/20 , H04L9/08 , H04L9/32
CPC分类号: G06F18/213 , G06F8/75 , G06F8/77 , G06F11/3003 , G06F11/3409 , G06F11/3433 , G06F11/3452 , G06F11/3466 , G06F16/211 , G06F16/2365 , G06F16/24573 , G06F16/24578 , G06F16/285 , G06F16/367 , G06F16/9024 , G06F16/9035 , G06F16/907 , G06F18/10 , G06F18/2115 , G06F18/2155 , G06N5/01 , G06N5/025 , G06N20/00 , G06N20/20 , H04L9/088 , H04L9/3236
摘要: A server system can receive an input identifying a problem to generate a solution using a machine-learning application. The method selects a machine-learning model template from a plurality of templates based at least in part on the input. The method analyzes one or more formats of the customer data to generate a customer data schema based at least in part a data ontology that applies to the identified problem. The method determines whether the customer data schema is misaligned with one or more key features of the selected machine-learning model template. Based on this determination, the method analyzes the metadata for the selected machine-learning model template to determine what additional information is required to re-align the customer data with the data expectations. The method can include gathering the addition information required to re-align the customer data with the data expectations of the selected machine-learning model template.
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6.
公开(公告)号:US11869661B2
公开(公告)日:2024-01-09
申请号:US16881928
申请日:2020-05-22
申请人: GRAIL, LLC
发明人: M. Cyrus Maher
IPC分类号: G16H50/20 , G16H50/70 , G16H50/30 , G06F18/2115 , C12Q1/6886
CPC分类号: G16H50/20 , G06F18/2115 , G16H50/30 , G16H50/70 , C12Q1/6886
摘要: Systems and methods for classifier training are provided. A first dataset is obtained that comprises, for each first subject, a corresponding plurality of bin values, each for a bin in a plurality of bins, and subject cancer condition. A feature extraction technique is applied to the first dataset thereby obtaining feature extraction functions, each of which is an independent linear or nonlinear function of bin values of the bins. A second dataset is obtained comprising, for each second subject, a corresponding plurality of bin values, each for a bin in the plurality of bins and subject cancer condition. The plurality of bin values of each corresponding subject in the second plurality are projected onto the respective feature extraction functions, thereby forming a transformed second dataset comprising feature values for each subject. The transformed second dataset and subject cancer condition serves to train a classifier on the cancer condition set.
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公开(公告)号:US11868385B2
公开(公告)日:2024-01-09
申请号:US16387362
申请日:2019-04-17
IPC分类号: G06F16/34 , G06F9/54 , G06F16/9038 , G06F18/2115 , G06F18/22 , G06F18/2431 , G06V10/70 , G06V10/764 , G06V10/80 , G06V20/40
CPC分类号: G06F16/345 , G06F9/542 , G06F16/9038 , G06F18/2115 , G06F18/22 , G06F18/2431 , G06V10/70 , G06V10/764 , G06V10/811 , G06V20/41
摘要: Systems, methods, and software described herein provide enhancements of managing summaries provided to end users. In one implementation, a summary service identifies data objects that correspond to an event and classifies each of the data objects into classifications of interest. Once classified, data objects are compared between the different classifications to identify information differences, and the information differences are used to generate a summary for an end user of the summary service.
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公开(公告)号:US11842257B2
公开(公告)日:2023-12-12
申请号:US16033448
申请日:2018-07-12
发明人: Senthil Nathan Rajendran , Selvarajan Kandasamy , Tejas Gowda BK , Mitali Sodhi , Gulshan Gaurav
CPC分类号: G06N20/20 , G06F18/2115 , G06F18/2193 , G06F18/285 , G06F18/40 , G06N20/00
摘要: System and method for predicting and scoring a data model are provided. The system includes a memory configured to receive a plurality of data sets. The system also includes a processing subsystem operatively coupled to the memory and configured to select one or more variables based on a plurality of parameters, to apply feature engineering and transformation on one or more variables to extract a plurality of features from the plurality of data sets, to build new data model based on the plurality of features, to evaluate a classification technique to select a right machine learning model based on a plurality of elements, to predict a newly built data model based on an evaluated classification technique and score the predicted data model. The system further includes a display model operatively coupled to the processing subsystem and configured to present the predicted and scored data model in one or more forms.
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公开(公告)号:US11829914B2
公开(公告)日:2023-11-28
申请号:US17680493
申请日:2022-02-25
申请人: Enlitic, Inc.
发明人: Kevin Lyman , Anthony Upton , Li Yao , Jordan Prosky , Eric C. Poblenz , Chris Croswhite , Ben Covington
IPC分类号: G16H30/20 , G06Q10/0631 , G16H10/60 , G16H30/40 , G16H15/00 , G06T5/00 , G06T5/50 , G06T7/00 , G06T11/00 , G06N5/04 , G06N20/00 , G06F9/54 , G06T7/187 , G06T7/11 , G06F3/0482 , G06T3/40 , A61B5/00 , G16H50/20 , G06F21/62 , G06Q20/14 , G16H40/20 , G06F3/0484 , G16H10/20 , G06N5/045 , G06T7/10 , G06T11/20 , G06F16/245 , G06T7/44 , G06N20/20 , H04L67/12 , H04L67/01 , G06V10/82 , G06F18/40 , G06F18/214 , G06F18/21 , G06F18/2115 , G06F18/2415 , G06V10/25 , G06V30/19 , G06V10/764 , G06V40/16 , G06V10/22 , G16H50/70 , G06T7/70 , G16H50/30 , A61B5/055 , A61B6/03 , A61B8/00 , A61B6/00 , G06Q50/22 , G06F40/295 , G06F18/24 , G06F18/2111 , G06V30/194
CPC分类号: G06Q10/06315 , A61B5/7264 , G06F3/0482 , G06F3/0484 , G06F9/542 , G06F16/245 , G06F18/214 , G06F18/217 , G06F18/2115 , G06F18/2415 , G06F18/41 , G06F21/6254 , G06N5/04 , G06N5/045 , G06N20/00 , G06N20/20 , G06Q20/14 , G06T3/40 , G06T5/002 , G06T5/008 , G06T5/50 , G06T7/0012 , G06T7/0014 , G06T7/10 , G06T7/11 , G06T7/187 , G06T7/44 , G06T7/97 , G06T11/001 , G06T11/006 , G06T11/206 , G06V10/225 , G06V10/25 , G06V10/764 , G06V10/82 , G06V30/19173 , G06V40/171 , G16H10/20 , G16H10/60 , G16H15/00 , G16H30/20 , G16H30/40 , G16H40/20 , G16H50/20 , H04L67/01 , H04L67/12 , A61B5/055 , A61B6/032 , A61B6/5217 , A61B8/4416 , G06F18/2111 , G06F18/24 , G06F40/295 , G06Q50/22 , G06T7/70 , G06T2200/24 , G06T2207/10048 , G06T2207/10081 , G06T2207/10088 , G06T2207/10116 , G06T2207/10132 , G06T2207/20076 , G06T2207/20081 , G06T2207/20084 , G06T2207/30004 , G06T2207/30008 , G06T2207/30016 , G06T2207/30061 , G06V30/194 , G06V2201/03 , G16H50/30 , G16H50/70
摘要: A medical scan header standardization system is operable to determine a plurality of counts for a plurality of entries of at least one of a standard set of fields for headers of a plurality of medical images. A standard set of header entries is determined for at least one of the standard set of fields based on including ones of the entries for the each of the standard set of fields with counts of the plurality of counts that compare favorably to a threshold. One of the standard set of header entries is selected to replace an entry of a field of a header of a medical image. A computer vision model is trained utilizing a training set of images that includes the medical image and the selected one of the standard set of header entries. Inference data for at least one new medical scan is generated based on utilizing the computer vision model.
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10.
公开(公告)号:US20230377317A1
公开(公告)日:2023-11-23
申请号:US18364569
申请日:2023-08-03
申请人: Argo AI, LLC
发明人: Jelena Frtunikj , Daniel Alfonsetti
IPC分类号: G06V10/774 , G06N20/00 , G06V10/20 , G06V20/56 , G06F18/214 , G06F18/2115 , G06V10/772 , G06V10/82 , G06V20/58
CPC分类号: G06V10/774 , G06N20/00 , G06V10/255 , G06V20/56 , G06F18/2148 , G06F18/2115 , G06V10/772 , G06V10/82 , G06V20/58 , G06V20/588 , G06V20/64
摘要: Systems and methods for selecting data for training a machine learning model using active learning are disclosed. The methods include receiving a plurality of unlabeled sensor data logs corresponding to surroundings of an autonomous vehicle and identifying one or more trends associated with a training dataset comprising a plurality of labeled data logs. The methods also include selecting a subset of the plurality of unlabeled sensor data logs that have an importance score greater than a threshold, the importance score being determined based on the one or more trends. The subset of the plurality of unlabeled sensor data logs is used for updating the machine learning model to generate an updated model.
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