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公开(公告)号：US11520972B2
公开(公告)日：2022-12-06
申请号：US16984245
申请日：2020-08-04
IPC分类号： G06F17/00 , G06F40/169 , G06F40/279
摘要： Aspects of the invention include resolving future reference identifiers for documents. Aspects of the invention include processing a document including a reference to a future event, wherein processing includes performing natural language processing (NLP) on the document, and identifying the reference to the future event included in the document. Aspects of the invention also include generating a future reference identifier for the reference to the future event, and responsive to processing an occurrence of the future event, resolving the future reference identifier by providing data from a subsequent document for the future event associated with the future reference identifier.
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公开(公告)号：US11347928B2
公开(公告)日：2022-05-31
申请号：US16939283
申请日：2020-07-27
IPC分类号： G06F40/131 , G06F40/103 , G06V30/414 , G06V30/416
摘要： Aspects of the invention include detecting and processing sections spanning processed document partitions by caching a document partition. The document partition includes metadata indicating that the document partition is a portion of a whole document. Aspects also include pairing a candidate paragraph from the document partition with a cached paragraph segment and determining, using a coherence model, a probability that the candidate paragraph and the cached paragraph segment constitute a semantically coherent paragraph. Aspects further include discarding the cached paragraph segment and processing the candidate paragraph and the cached paragraph segment separately based on a determination that the probability is less than a threshold level and processing the candidate paragraph and the cached paragraph segment together as a cross-partition paragraph based on a determination that the probability is greater than the threshold level.
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公开(公告)号：US12118812B2
公开(公告)日：2024-10-15
申请号：US16942836
申请日：2020-07-30
发明人： Igor S. Ramos , Marc Dickenson , Andrew J Lavery
CPC分类号： G06V30/40 , G06F16/27 , G06Q10/10 , G06Q30/0185 , G06T7/0002 , G06V10/44 , G06Q40/02 , G06T2207/30176
摘要： An image of a financial instrument can be received and analyzed. One or more properties of the financial instrument can be determined based on the image. A uniqueness rating of the financial instrument can be determined based on the property or properties. Terms of the financial instrument are identified, and the financial instrument and identified terms are recorded in a digital storage base.
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公开(公告)号：US11693526B2
公开(公告)日：2023-07-04
申请号：US17111142
申请日：2020-12-03
发明人： Andrew J Lavery , James Lee Lentz , Shunguo Yan
IPC分类号： G06F3/0481 , G06N20/00 , G06F16/9035
CPC分类号： G06F3/0481 , G06F16/9035 , G06N20/00
摘要： A method, computer system, and a computer program product for modifying a user interface. Attributes of a source object identified by a user in connection with a user input for storing the source object are determined. Attributes of one or more target storage locations are determined. A target storage location for storing the source object is predicted, along with a confidence value associated with the prediction. The prediction is made using a machine learning model that predicts the predicted target storage location and associated confidence value based on the determined attributes of the source object. A plurality of target storage location usage patterns are determined. The user interface is modified based on the predicted target storage location.
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公开(公告)号：US20220179932A1
公开(公告)日：2022-06-09
申请号：US17111142
申请日：2020-12-03
发明人： Andrew J Lavery , James Lee Lentz , Shunguo Yan
IPC分类号： G06F21/31 , G06F21/78 , G06F16/9035 , G06N20/00
摘要： A method, computer system, and a computer program product for modifying a user interface. Attributes of a source object identified by a user in connection with a user input for storing the source object are determined. Attributes of one or more target storage locations are determined. A target storage location for storing the source object is predicted, along with a confidence value associated with the prediction. The prediction is made using a machine learning model that predicts the predicted target storage location and associated confidence value based on the determined attributes of the source object. A plurality of target storage location usage patterns are determined. The user interface is modified based on the predicted target storage location.
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