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1.发明公开公开(公告)号：US20230186171A1
公开(公告)日：2023-06-15
申请号：US17643904
申请日：2021-12-13
发明人： Gena WOMACK , Tania Cruz MORALES
IPC分类号： G06N20/20 , G06F16/23 , G06F3/0483 , G06F3/04842
CPC分类号： G06N20/20 , G06F3/0483 , G06F3/04842 , G06F16/2379
摘要： A method of for analyzing data using machine learning models comprising: receiving data associated with a request to add a new occasion to an electronic database, wherein: the electronic database includes a plurality of occasions; a portion of the plurality of occasions is associated with a timing value and a substance value; the electronic database is associated with a first progress value; and the data associated with the request to add the new occasion is at least partially automatically generated by a first trained machine learning model; receiving data associated with the new occasion; predicting, by a second trained machine learning model, a timing value and a substance value for the new occasion; calculating a second progress value based on the timing value and the substance value for the new occasion; and causing a graphical user interface to display a notification to add the new occasion to the electronic database.
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公开(公告)号：US20240169329A1
公开(公告)日：2024-05-23
申请号：US18058172
申请日：2022-11-22
发明人： Jennifer KWOK , Tania Cruz MORALES , Sara Rose BRODSKY , Abhay DONTHI , Joshua EDWARDS , Jason ZWIERZYNSKI
IPC分类号： G06Q20/10
CPC分类号： G06Q20/102
摘要： A method for machine-learning based action generation, and more specifically, using machine-learning to dynamically adjust financial account payments and fees. The method may comprise: receiving user data; determining whether a trigger condition has been met; upon determining that a trigger condition has been met, generating, using a trained machine-learning model, one or more actions based on the user data associated with the user, wherein the trained machine-learning model has been trained based on (i) training user data and (ii) training action data, to learn relationships between the training user data and the training actions data, such that the trained machine-learning model is configured to use the learned relationships to generate one or more actions in response to input of the user data associated with the user; selecting a first action of the one or more actions; and automatically executing the first action.
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公开(公告)号：US20240232890A9
公开(公告)日：2024-07-11
申请号：US18049092
申请日：2022-10-24
发明人： Jennifer KWOK , Sara Rose BRODSKY , Jason ZWIERZYNSKI , Joshua EDWARDS , Abhay DONTHI , Tania Cruz MORALES
CPC分类号： G06Q20/4016 , G06Q20/382
摘要： Systems and methods for external account authentication are disclosed herein. They include receiving a call to pair the external account with a secure account, extracting external data from the external account, the external data corresponding to external account content, providing user activity data from the secure account as an input to an authentication machine learning model, providing the external data as an input to the authentication machine learning model, the authentication machine learning model configured to output a certainty level that the external account is associated with a user of the secure account based on the external data and the activity data, receiving the certainty level from the authentication machine learning model, determining that the certainty level meets a certainty threshold, and pairing the external account with the secure account based on determining that the certainty level meets the certainty threshold.
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公开(公告)号：US20240202572A1
公开(公告)日：2024-06-20
申请号：US18066361
申请日：2022-12-15
CPC分类号： G06N20/00 , G06F18/217
摘要： Systems and methods for documenting label versions for machine learning model input datasets are disclosed herein. The system may receive a label modification request for a dataset. The system may determine a dataset identifier and model error indicator. The system may determine a modification timestamp. The system may generate a label record and generate the label record in a label record database.
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5.发明公开公开(公告)号：US20240202457A1
公开(公告)日：2024-06-20
申请号：US18066316
申请日：2022-12-15
IPC分类号： G06F40/35 , G06F40/117 , G06N3/09 , H04L51/02
CPC分类号： G06F40/35 , G06F40/117 , G06N3/09 , H04L51/02
摘要： Systems and methods for labeling data for artificial neural network models when data is received as real-time data and batch-processed data are disclosed herein. The system may receive a first data stream. The system may generate a first vector representation of the first real-time processed data. The system may determine the first label for a first training datum. The system may determine whether all data used to generate a final label has been received. The system may assign a first label type to the first label.
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6.发明公开公开(公告)号：US20240202228A1
公开(公告)日：2024-06-20
申请号：US18067331
申请日：2022-12-16
CPC分类号： G06F16/355 , G06F16/38
摘要： System and methods disclosed herein are for generating labels for dynamically received textual data based on similarity with previously-labeled datasets. The system may receive first textual data. The system may determine a first timestamp at which the first textual data was received. The system may determine a first receipt range for the first textual data based on the first timestamp. The system may retrieve a plurality of datasets. The system may select a first dataset from the plurality of datasets. The system may retrieve second textual data from the first dataset. The system may determine a first similarity metric between the first textual data and the first dataset. The system may compare the first similarity metric to a threshold similarity metric. The system may determine to assign a label for the second textual data to the first textual data.
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7.发明公开公开(公告)号：US20240169324A1
公开(公告)日：2024-05-23
申请号：US18057446
申请日：2022-11-21
发明人： Joshua EDWARDS , Jason ZWIERZYNSKI , Abhay DONTHI , Sara Rose BRODSKY , Jennifer KWOK , Tania Cruz MORALES
IPC分类号： G06Q10/10
CPC分类号： G06Q10/1097
摘要： A method for executing actions based on event data using machine learning is disclosed. The method comprises: receiving occasion data associated with a user; analyzing, using a trained machine learning model, the occasion data to identify an occasion associated with a first classification, wherein the trained machine learning model has been trained based on (i) training occasion data that includes information regarding one or more occasions associated with the training occasion data and (ii) training classification data that includes a prior classification for each of the occasions, to learn relationships between the training occasion data and the training classification data, such that the trained machine learning model is configured to use the learned relationships to identify an occasion associated with a first classification in response to input of the occasion data; determining an action based on the occasion associated with the first classification; and automatically executing the action.
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公开(公告)号：US20240135381A1
公开(公告)日：2024-04-25
申请号：US18049092
申请日：2022-10-23
发明人： Jennifer KWOK , Sara Rose BRODSKY , Jason ZWIERZYNSKI , Joshua EDWARDS , Abhay DONTHI , Tania Cruz MORALES
CPC分类号： G06Q20/4016 , G06Q20/382
摘要： Systems and methods for external account authentication are disclosed herein. They include receiving a call to pair the external account with a secure account, extracting external data from the external account, the external data corresponding to external account content, providing user activity data from the secure account as an input to an authentication machine learning model, providing the external data as an input to the authentication machine learning model, the authentication machine learning model configured to output a certainty level that the external account is associated with a user of the secure account based on the external data and the activity data, receiving the certainty level from the authentication machine learning model, determining that the certainty level meets a certainty threshold, and pairing the external account with the secure account based on determining that the certainty level meets the certainty threshold.
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