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公开(公告)号：US20230222288A1
公开(公告)日：2023-07-13
申请号：US17571761
申请日：2022-01-10
IPC分类号： G06F40/205 , G06F40/284
CPC分类号： G06F40/205 , G06F40/284
摘要： Systems for partitioning text are disclosed. The system can receive a text string. A delimiter can be identified based on the text string. Based on identifying the delimiter, a character sequence to the left and/or right of the delimiter can be identified. The identification can occur up to a predetermined number/length of characters. Using a trained model, the system can determine whether the character sequence indicates the delimiter is part of a continuous string of text. Based on determining whether or not the delimiter is part of the continuous string of text, the system can generate a token representing the continuous string of text or the delimiter.
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公开(公告)号：US20230006908A1
公开(公告)日：2023-01-05
申请号：US17364344
申请日：2021-06-30
摘要： Provided herein are systems and methods for sanitizing logged data packets in a distributed system prior to storing them in a remote or third-party data server. Interactions with an application are monitored and values in a data packet are extracted from the interaction. The values are classified based on a classification configuration and respective labels of the values. The values are then sanitized based on the classification to prevent exposure of secure or private data. The sanitized data packets are then logged into the remote data server. The logged data can be used to help resolve events occurring in the application. The classification configuration can be iteratively updated and the interactions repeated to capture data that was previously sanitized to aid in resolution of events. The logged data can also be used in research or analysis, such as for identifying potential improvements to the application.
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公开(公告)号：US20230275826A1
公开(公告)日：2023-08-31
申请号：US18143258
申请日：2023-05-04
CPC分类号： H04L43/10 , H04L43/50 , H04L63/0471 , H04L63/166
摘要： Provided herein are systems and methods for sanitizing logged data packets in a distributed system prior to storing them in a remote or third-party data server. Interactions with an application are monitored and values in a data packet are extracted from the interaction. The values are classified based on a classification configuration and respective labels of the values. The values are then sanitized based on the classification to prevent exposure of secure or private data. The sanitized data packets are then logged into the remote data server. The logged data can be used to help resolve events occurring in the application. The classification configuration can be iteratively updated and the interactions repeated to capture data that was previously sanitized to aid in resolution of events. The logged data can also be used in research or analysis, such as for identifying potential improvements to the application.
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公开(公告)号：US20220345425A1
公开(公告)日：2022-10-27
申请号：US17811063
申请日：2022-07-06
发明人： Minh LE , Erik MUELLER , Rui ZHANG
摘要： Methods and systems are described for generating dynamic interface options using machine learning models. The dynamic interface options may be generated in real time and reflect the likely goals and/or intents of a user. The machine learning model may provide these features by interpreting multi-modal feature inputs. For example, the machine learning model may include a first machine learning model, wherein the first machine learning model comprises a convolutional neural network, and a second machine learning model, wherein the second machine learning model performs a Weight of Evidence (WOE) analysis.
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5.发明申请公开(公告)号：US20210399999A1
公开(公告)日：2021-12-23
申请号：US16908116
申请日：2020-06-22
发明人： Kunlaya SOIAPORN , Victor ALVAREZ MIRANDA , Pamela KATALI , Arturo HERNANDEZ ZELEDON , Rui ZHANG , Kwan-Yuet HO
摘要： Methods and systems are described for generating dynamic conversational responses using two-tier machine learning models. The dynamic conversational responses may be generated in real time and reflect the likely goals and/or intents of a user. The two-tier machine learning model may include a first tier that determines an intent cluster based on a feature input, and a second tier that determines a specific intent from the cluster.
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6.发明公开公开(公告)号：US20240046922A1
公开(公告)日：2024-02-08
申请号：US18489482
申请日：2023-10-18
IPC分类号： G10L15/18 , G10L15/065 , G10L15/22 , G06F40/30
CPC分类号： G10L15/1822 , G10L15/065 , G10L15/22 , G06F40/30 , G10L15/1815 , G10L2015/221 , G10L2015/227 , G10L15/16
摘要： Methods and systems for dynamically updating machine learning models that provide conversational responses through the use of a configuration file that defines modifications and changes to the machine learning model are disclosed. For example, the configuration file may be used to define an expected behavior and required attributes for instituting modifications and changes (e.g., via a mutation algorithm) to the machine learning model.
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公开(公告)号：US20220414684A1
公开(公告)日：2022-12-29
申请号：US17356084
申请日：2021-06-23
发明人： Minh LE , Rui ZHANG , Erik MUELLER , Victor Alvarez MIRANDA
摘要： Provided herein are systems and methods for using multi-modal regression to predict customer intent to contact a merchant. Multi-modal data including numerical data and unstructured data are extracted from customer interactions with the merchant. Features of the numerical data and the unstructured data are separately extracted and classified using techniques specific to the data types. The features for each type are then separately used to predict probabilities of customer intent. A neural network is used to combine the predictions into a single set of estimates of customer intent. This set of estimates of customer intents is used to estimate a probability that the customer will contact the merchant. The customer is then contacted based on the estimate.
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公开(公告)号：US20240232695A1
公开(公告)日：2024-07-11
申请号：US18094644
申请日：2023-01-09
IPC分类号： G06N20/00
CPC分类号： G06N20/00
摘要： Provided herein are systems and methods for detecting and sanitizing sensitive data using domain-enhanced attention neural networks. In some embodiments, a processor of a client retrieves training data comprising tuples. Each tuple comprises a first parameter and a second parameter. For each tuple the processor matches a substring in a first parameter of the respective tuple to a keyword of a plurality of keywords, identifies a security category corresponding to the at least one keyword, and expands the first parameter of the respective tuple to comprise a respective string associated with the application event and the security category. The processor trains a model to detect and sanitize the sensitive data from the application events using the tuples, including an expanded first parameter for each tuple. The processor sanitizes the sensitive data using the trained model.
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9.发明公开公开(公告)号：US20230412532A1
公开(公告)日：2023-12-21
申请号：US18459828
申请日：2023-09-01
发明人： Kunlaya SOIAPORN , Victor ALVAREZ MIRANDA , Pamela KATALI , Arturo HERNANDEZ ZELEDON , Rui ZHANG , Kwan-Yuet HO
CPC分类号： H04L51/02 , G10L15/16 , G06N20/20 , G06F18/23 , G06V10/763
摘要： Methods and systems are described for generating dynamic conversational responses using two-tier machine learning models. The dynamic conversational responses may be generated in real time and reflect the likely goals and/or intents of a user. The two-tier machine learning model may include a first tier that determines an intent cluster based on a feature input, and a second tier that determines a specific intent from the cluster.
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10.发明公开公开(公告)号：US20230267280A1
公开(公告)日：2023-08-24
申请号：US18306196
申请日：2023-04-24
发明人： Victor Alvarez MIRANDA , Rui ZHANG
CPC分类号： G06F40/35 , H04L51/02 , G06Q20/108 , G06N20/00 , G06F3/04883
摘要： Methods and systems are described for generating dynamic conversational responses using machine learning models. The dynamic conversational responses may be generated in real time and reflect the likely goals and/or intents of a user. The machine learning model may provide these features by monitoring one or more user actions and/or lengths of time between one or more user actions during conversational interactions.
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