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公开(公告)号：US20240364814A1
公开(公告)日：2024-10-31
申请号：US18608356
申请日：2024-03-18
发明人： Jigar Mehta , Abbey Chaver , Paul Zeng , Abhi Sharma
IPC分类号： H04M3/51 , H04L51/02 , H04L51/046
CPC分类号： H04M3/5191 , H04L51/02 , H04L51/046
摘要： The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to determine predicted client intent classifications and/or client-agent escalation classes to generate personalized digital text reply options within an automated interactive digital text thread. For example, disclosed systems utilize the machine learning model to generate predicted client-agent escalation classes and corresponding probabilities. The disclosed systems utilize the predicted client-agent escalation classifications and the escalation class probabilities to generate personalized digital text reply options. Moreover, the disclosed systems can provide personalized digital text reply options to a client device within an automated interactive digital text thread, bypassing the inefficiency of menu options or protocols utilized to guide clients to terminal information.
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2.发明公开公开(公告)号：US20240361990A1
公开(公告)日：2024-10-31
申请号：US18656297
申请日：2024-05-06
发明人： Jason Fox , Ben Snyder , Harold Kim
IPC分类号： G06F8/20
CPC分类号： G06F8/20
摘要： This disclosure describes one or more embodiments of methods, non-transitory computer-readable media, and systems provide developer tools for generating and providing visualizations of data densities for various portions of a computer application. For example, the disclosed systems can determine a data density that reflects a ratio or an amount of data presented within a (portion of a) display window relative to the entire (portion of the) display window. The disclosed systems can further provide a visual representation of a data density for display on a client device, along with indications or suggestions for how to improve (e.g., reduce or increase) the data density for better comprehensibility (e.g., upon distribution of the application). In certain embodiments, the disclosed systems can generate suggestions based on an application type of a computer application in development and/or based on a function associated with a particular (portion of a) display window.
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3.发明公开公开(公告)号：US20240346577A1
公开(公告)日：2024-10-17
申请号：US18595003
申请日：2024-03-04
发明人： Erin Xie , Aashna Agarwal , Aoni Wang , Braden Staudacher , Dennis Jiang , Lucy Liu
IPC分类号： G06Q40/02
CPC分类号： G06Q40/02
摘要： The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize a variety of machine learning models and a base limit value model to generate user interface elements that transparently and efficiently present current and future base limit values for user accounts. For example, the disclosed systems can utilize a machine learning model to determine a base limit value, subsequent base limit value, and user activity conditions to achieve the subsequent base limit value for a user account. Then, the disclosed systems can display a base limit progress element that indicates progress towards fulfilling the user activity conditions to achieve the subsequent base limit value. For example, the disclosed systems can display, within a graphical user interface, multiple base limit progress elements that indicate progress towards fulfilling the user activity conditions in separate time-based segments (e.g., to represent time elements within the user activity conditions).
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公开(公告)号：US12079787B2
公开(公告)日：2024-09-03
申请号：US17821992
申请日：2022-08-24
发明人： Roman Lisagor , Praveen Jain , Fletcher Fowler
摘要： This disclosure describes a vector configurator system that, as part of an inter-network facilitation system, can generate and utilize transaction vectors for facilitating network transactions among digital accounts of an inter-network facilitation system. For example, the disclosed systems can generate a transaction vector that includes a unique set of key attributes defining parameters for a network transaction and that further includes a ledger specification indicating computer code for how to execute the network transaction. Upon receiving a transaction request, the disclosed systems can access a repository of transaction vectors to determine whether a corresponding vector exists for the request. The disclosed systems can either validate or reject the transaction request based on the existence or nonexistence of a transaction vector for the request. The disclosed systems can further execute or process a network transaction for the request according to a stored transaction vector corresponding to the request.
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公开(公告)号：US20240256417A1
公开(公告)日：2024-08-01
申请号：US18160003
申请日：2023-01-26
发明人： Rachna Bafna , Joshua Sacks , Ram Daga , Clinton Williams
IPC分类号： G06F11/34 , G06F3/0481 , G06F3/04847 , G06F11/30
CPC分类号： G06F11/3414 , G06F3/0481 , G06F3/04847 , G06F11/3006 , G06F11/3447 , G06F11/3457
摘要： The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating computing-resource metrics based on limited user inputs provided through user interaction with a graphical user interface and then performing a system load test simulating system performance. In particular, in one or more embodiments, the disclosed systems provide a graphical user interface that requires limited user input to generate a load test that can determine computing-resource metrics based on user input of a load test script, test dataset, target throughput metric, and a load metric.
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公开(公告)号：US12010075B2
公开(公告)日：2024-06-11
申请号：US17809765
申请日：2022-06-29
发明人： Jigar Mehta , Abbey Chaver , Abhi Sharma , Sashidhar Guntury
IPC分类号： H04L51/02 , G06F16/35 , H04L51/046
CPC分类号： H04L51/02 , G06F16/355 , H04L51/046
摘要： The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a machine learning model to determine predicted client intent classifications and generate personalized digital text reply options within an automated interactive digital text thread. For example, disclosed systems utilize the machine learning model to generate predicted client intent classifications and corresponding intent classification probabilities. The disclosed systems utilize the predicted client disposition classifications and the disposition classification probabilities to generate personalized digital text reply options. Moreover, the disclosed systems can provide personalized digital text reply options to a client device within an automated interactive digital text thread, bypassing the inefficiency of menu options or protocols utilized to guide clients to terminal information.
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公开(公告)号：US20240168800A1
公开(公告)日：2024-05-23
申请号：US18057874
申请日：2022-11-22
CPC分类号： G06F9/4881 , G06F9/3879 , G06F9/44505 , G06F9/541
摘要： The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that dynamically execute data source agnostic data pipeline job configurations that can interact with a variety of data sources while utilizing a unified request format. In particular, the disclosed systems can facilitate a data pipeline framework that utilizes source connectors for data sources, target connectors for data sources, and data transformations in data pipeline job configurations to build various data pipelines. For instance, the disclosed systems can utilize a data pipeline job configuration that includes requests for a data source in a given language with various other data pipeline functionalities via data source connectors specified within the data pipeline job configuration. For example, the disclosed systems can utilize a data source connector to map data source requests to native code commands for the data source to read or write data in relation to the data source.
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8.发明公开公开(公告)号：US20240152926A1
公开(公告)日：2024-05-09
申请号：US18052423
申请日：2022-11-03
IPC分类号： G06Q20/40
CPC分类号： G06Q20/4016
摘要： The present disclosure relates to systems, non-transitory computer-readable media, and methods for managing fraud-risk in digital networks utilizing an intelligently trained fraud-risk tiering system. In particular, in one or more embodiments, the disclosed systems utilize one or more fraud-risk tiering models to determine an initial risk tier for a digital account from a plurality of risk tiers based on attributes received upon creation of the digital account. Further, in one or more embodiments, the disclosed systems utilize one or more fraud-risk tiering models to determine an updated risk tier for the digital account further based on account usage data. In some embodiments, the disclosed systems utilize one or more machine learning models for initial and ongoing fraud-risk assessment of digital accounts.
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公开(公告)号：US11966887B1
公开(公告)日：2024-04-23
申请号：US18050252
申请日：2022-10-27
发明人： Richard Vorp , Geetha Premraj , Michael Ducker , Priyanka Seth , Praveen Jain , Fletcher Fowler
CPC分类号： G06Q20/027 , G06Q20/108
摘要： The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that enable network transactions between user accounts belonging to different network transaction platforms and unify user account modifications across user accounts on different network transaction platforms. For instance, the disclosed systems can cause two or more network transaction platforms to transmit and/or receive transactional values from one or more intermediary holding accounts upon receiving network transaction requests between user accounts belonging to the two or more network transaction platforms. In addition, the disclosed system can unify user account data on different network transaction platforms by modifying or updating the user account data (e.g., user attributes) by propagating schematized data messages to the one or more other network transaction platforms with instructions to modify or update the user attributes on other user accounts related to the user account.
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10.发明公开公开(公告)号：US20240007421A1
公开(公告)日：2024-01-04
申请号：US17809765
申请日：2022-06-29
发明人： Jigar Mehta , Abbey Chaver , Abhi Sharma , Sashidhar Guntury
IPC分类号： H04L51/02 , H04L51/046 , H04L41/026 , H04L41/50 , G06F16/35
CPC分类号： H04L51/02 , H04L51/046 , H04L41/026 , H04L41/5093 , G06F16/355
摘要： The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a machine learning model to determine predicted client intent classifications and generate personalized digital text reply options within an automated interactive digital text thread. For example, disclosed systems utilize the machine learning model to generate predicted client intent classifications and corresponding intent classification probabilities. The disclosed systems utilize the predicted client disposition classifications and the disposition classification probabilities to generate personalized digital text reply options. Moreover, the disclosed systems can provide personalized digital text reply options to a client device within an automated interactive digital text thread, bypassing the inefficiency of menu options or protocols utilized to guide clients to terminal information.
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