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公开(公告)号：US11693879B2
公开(公告)日：2023-07-04
申请号：US17324667
申请日：2021-05-19
发明人： Paul O'Hara , Ying Wu , Jiazheng Li , Cathal McGovern , Malte Christian Kaufmann , Esther Rodrigo Ortiz , Kerry O'Connor , Michael Golden , Satinder Singh , Vlad Zat
IPC分类号： G06F16/26 , G06F16/2458
CPC分类号： G06F16/26 , G06F16/2465
摘要： Systems and methods include reception of a set of data including continuous features and a discrete feature, each continuous feature associated with a plurality of values and the discrete feature associated with a plurality of discrete values, determine, for each continuous feature, a relationship factor representing a relationship between the discrete feature and the continuous feature based on the plurality of values associated with the continuous feature and the plurality of discrete values, identify one of the continuous features associated with a largest one of the determined relationship factors, generate, for each of the other features, a correlation factor representing a correlation between the continuous feature and the identified continuous feature, determine, for each of the continuous features other than the identified continuous feature, a composite relationship score based on the relationship factor and the correlation factor associated with the feature, and present a visualization associated with the discrete feature, the identified continuous feature, and a continuous feature associated with a largest composite relationship score.
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公开(公告)号：US20240062101A1
公开(公告)日：2024-02-22
申请号：US17890073
申请日：2022-08-17
发明人： Paul O'Hara
IPC分类号： G06N20/00
CPC分类号： G06N20/00
摘要： A historical feature contribution score dataset comprising a number of sets of scores generated by machine learning model may be obtained. Additional feature contribution score sets may be materialized such that the size of each additional feature contribution score set is based on a corresponding randomly selected values within a set-size range. A training dataset may be produced that includes feature contribution scores and corresponding classification labels extracted from the historical feature contribution score dataset and the additional feature contribution score sets. The classification labels may indicate an amount that the corresponding feature contribution scores contribute to a prediction of a target feature. A machine learning model may be trained to predict the classification labels using the training dataset. An input feature contribution score set may be applied to the machine learning model to obtain predicted classification labels.
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公开(公告)号：US20240020896A1
公开(公告)日：2024-01-18
申请号：US18351288
申请日：2023-07-12
IPC分类号： G06T11/20 , G06F18/2431
CPC分类号： G06T11/206 , G06F18/2431
摘要： Using approximated bin intervals to label the histograms provides clarity and allows for the histogram to be more intuitively understood. A dataset may comprise a plurality of records having a plurality of features including one or more continuous features. A selection of a continuous feature may be obtained. A bin width based on a number of bins and feature statistics of the continuous feature may be determined. An approximated bin interval range is determined by applying a bin mask based on the bin width to the feature statistics. An approximated bin width is determined based on the number of bins and the approximated bin interval range. Approximated bin intervals for the histogram are determined based on the approximated bin width. A histogram is generated having bins with intervals based the approximated bin intervals.
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公开(公告)号：US11321332B2
公开(公告)日：2022-05-03
申请号：US16876463
申请日：2020-05-18
发明人： Paul O'Hara , Ying Wu , Esther Rodrigo Ortiz , Paul O'Connor , Gabor Szabo , Artur Stulka
IPC分类号： G06F16/00 , G06F16/2458 , G06F17/18 , G06F16/23
摘要： The present disclosure involves systems, software, and computer implemented methods for automatically recommending one or more frequencies for time series data. One example method includes receiving a request for an insight analysis for an input time series included in a dataset. For each of multiple frequencies to analyze, the input time series is transformed into a frequency time series. An absolute percentage change impact factor and an absolute trend impact factor are determined for each frequency time series. A frequency interest score is determined based on the determined absolute percentage change factors and the determined absolute trend impact factors, for each time frequency time series. The frequency interest score is provided for at least some of the frequency time series.
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公开(公告)号：US20240193462A1
公开(公告)日：2024-06-13
申请号：US18059852
申请日：2022-11-29
发明人： Paul O'Hara
CPC分类号： G06N20/00 , G06K9/6256
摘要： A system may obtain a plurality of historical feature contribution score (FCS) datasets, each historical FCS dataset comprising a first plurality of feature contribution scores and a size of the historical FCS dataset. The system may apply default feature contribution category classification (FCCC) parameters to the plurality of historical FCS datasets and may optimize the default FCCC parameters to produce a plurality of optimized FCCC parameters. The system may produce a training dataset comprising the optimized FCCC parameters and use the training dataset to train a machine learning model to apply the category classification labels. The system may apply the new FCS dataset to the machine learning model, the new FCS dataset comprising a second plurality of feature contribution scores and a size of the new FCS dataset, and provide the category classification labels for the new FCS dataset to a user interface.
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公开(公告)号：US11720579B2
公开(公告)日：2023-08-08
申请号：US17367882
申请日：2021-07-06
IPC分类号： G06F16/2458 , G06F16/28 , G06F16/2457
CPC分类号： G06F16/2462 , G06F16/2457 , G06F16/283
摘要： Systems and methods include determination, for each of a plurality of discrete features, of statistics based on a number of occurrences of each discrete value of the discrete feature in the data, determination of first summary statistics based on the determined statistics, determine of a dissimilarity for each discrete feature based on the first summary statistics and on the statistics determined for the discrete feature, determination of candidate discrete features based on the determined dissimilarities, determination, for each of the candidate discrete features, of second summary statistics based on values of a continuous feature associated with each discrete value of the candidate discrete feature, determination of a deviation score for each of the candidate discrete features based on the second summary statistics, and transmission of the candidate discrete features for display in association with the continuous feature based on the determined deviation scores.
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公开(公告)号：US20210357417A1
公开(公告)日：2021-11-18
申请号：US16876441
申请日：2020-05-18
发明人： Ying Wu , Paul O'Connor , Esther Rodrigo Ortiz , Artur Stulka , Mateusz Lewandowski , Paul Sheedy , Mairtin Keane , Paul O'Hara , Malte Christian Kaufmann , Robert McGrath
IPC分类号： G06F16/2457 , G06F16/2458 , G06F16/215 , G06F16/22
摘要： The present disclosure involves systems, software, and computer implemented methods for ranking time dimensions. One example method includes receiving a request for an insight analysis for a dataset that includes a value dimension and a set of multiple date dimensions. Each date dimension is converted into a time series and a value quality factor is determined for each time series that represents a level of data quality for the time series. A time series informative factor is determined for each time series that represents how informative the time series is within a specified time window. An insight score is determined, for each time dimension, based on the determined value quality factors and the determined time series informative factors. The insight score for the time dimension is provided, for at least some of the time dimensions.
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公开(公告)号：US20210349911A1
公开(公告)日：2021-11-11
申请号：US16867036
申请日：2020-05-05
发明人： Ben Murphy , Ying Wu , Paul O'Hara , Emmet Norton , Malte Christian Kaufmann , Orla Cullen
摘要： The present disclosure involves systems, software, and computer implemented methods for automatically detecting hot areas in heat map visualizations. One example method includes identifying a two-dimensional heat map. The identified two-dimensional heat map is converted to a one-dimensional heat map. Cells of the one-dimensional heat map are clustered using a density-based clustering algorithm to generate at least one dense region of cells. A mean value of cells in each dense region is calculated and the dense regions are sorted by mean value in descending order. An approach for identifying hot areas is selected and the selected approach is used to identify at least one dense region as a hot area of the one-dimensional heat map.
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公开(公告)号：US11734864B2
公开(公告)日：2023-08-22
申请号：US17514801
申请日：2021-10-29
IPC分类号： G06T11/20 , G06F18/2431
CPC分类号： G06T11/206 , G06F18/2431
摘要： Using approximated bin intervals to label the histograms provides clarity and allows for the histogram to be more intuitively understood. A dataset may comprise a plurality of records having a plurality of features including one or more continuous features. A selection of a continuous feature may be obtained. A bin width based on a number of bins and feature statistics of the continuous feature may be determined. An approximated bin interval range is determined by applying a bin mask based on the bin width to the feature statistics. An approximated bin width is determined based on the number of bins and the approximated bin interval range. Approximated bin intervals for the histogram are determined based on the approximated bin width. A histogram is generated having bins with intervals based the approximated bin intervals.
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公开(公告)号：US11681715B2
公开(公告)日：2023-06-20
申请号：US17342812
申请日：2021-06-09
IPC分类号： G06F16/2458 , G06F16/28
CPC分类号： G06F16/2462 , G06F16/2465 , G06F16/285
摘要： Systems and methods include determination, determine, for each of a plurality of discrete features, of statistics for each discrete value of the discrete feature based on values of a continuous feature associated with the discrete value, determination, for each discrete feature, of first summary statistics based on the statistics determined for each discrete value of the discrete feature, determination, for each discrete feature, of a dissimilarity based on the first summary statistics determined for the discrete feature and on the statistics determined for each discrete value of the discrete feature, determination of candidate discrete features of the discrete features based on the determined dissimilarities, the candidate discrete features comprising less than all of the discrete features, determination, for each of the candidate discrete features, of second summary statistics based on values of the continuous feature associated with each discrete value of the candidate discrete feature, determine of a deviation score for each of the candidate discrete features based on the second summary statistics, and presentation of the candidate discrete features based on the determined deviation scores.
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