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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
Applicant: Business Objects Software Ltd.
Inventor： Paul O'Hara , Malte Christian Kaufmann , Esther Rodrigo Ortiz , Conor White
IPC: G06T11/20 , G06F18/2431
CPC classification number: G06T11/206 , G06F18/2431
Abstract: 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
Applicant: BUSINESS OBJECTS SOFTWARE LTD.
Inventor： Paul O'Hara , Malte Christian Kaufmann , Anirban Banerjee , Ian Denver , Alan McShane
IPC: G06F16/2458 , G06F16/28
CPC classification number: G06F16/2462 , G06F16/2465 , G06F16/285
Abstract: 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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公开(公告)号：US20210357401A1
公开(公告)日：2021-11-18
申请号：US16876463
申请日：2020-05-18
Applicant: BUSINESS OBJECTS SOFTWARE LTD.
Inventor： Paul O'Hara , Ying Wu , Esther Rodrigo Ortiz , Paul O'Connor , Gabor Szabo , Artur Stulka
IPC: G06F16/2458 , G06F16/23 , G06F17/18
Abstract: 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：US20200098055A1
公开(公告)日：2020-03-26
申请号：US16140760
申请日：2018-09-25
Applicant: BUSINESS OBJECTS SOFTWARE LTD.
Inventor： Paul O'Hara , Ying Wu
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, to predict future Day Sales Outstanding (DSO) forecasts for a number of future time periods. In one aspect, a method includes receiving open receivables financial line item data and revenue financial line item data, providing the open receivables financial line item data to a DSO predictor engine to generate a predicted open receivables that includes a multi-step time series forecasting regression generated from the open receivables financial line item data, providing the revenue financial line item data to the DSO predictor engine to generate a predicted revenue comprising the multi-step time series forecasting regression generated from the revenue financial line item data; generating a predicted DSO with the predicted open receivables and predicted revenue, and providing the predicted DSO to a client device.
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公开(公告)号：US12050628B1
公开(公告)日：2024-07-30
申请号：US18348143
申请日：2023-07-06
Applicant: Business Objects Software Ltd.
Inventor： Paul O'Hara , Ying Wu , Malte Christian Kaufmann
CPC classification number: G06F16/285 , G06F16/2365
Abstract: Anomalies may be detected using a multiple machine learning model anomaly detection framework. A clustering model is trained using an unsupervised machine learning algorithm on a historical anomaly dataset. A plurality of clusters of records are determined by applying the historical anomaly dataset to the clustering model. Then it is determined whether each cluster of the plurality of clusters is an anomaly-type cluster or a normal-type cluster. The plurality of labels for the plurality of records are updated based on the particular record's cluster classification. Non-pure clusters are determined from among the plurality of clusters based on a purity threshold. A supervised machine learning model is trained for each of the non-pure clusters using the records in the given cluster and the labels for each of those records. Then, predictions of an anomaly are made using the clustering model and the supervised machine learning models.
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Patent Agency Ranking
公开(公告)号：US11727030B2
公开(公告)日：2023-08-15
申请号：US16867036
申请日：2020-05-05
Applicant: BUSINESS OBJECTS SOFTWARE LTD.
Inventor： Ben Murphy , Ying Wu , Paul O'Hara , Emmet Norton , Malte Christian Kaufmann , Orla Cullen
CPC classification number: G06F16/258 , G06F7/14 , G06F16/285
Abstract: 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：US20230133856A1
公开(公告)日：2023-05-04
申请号：US17514801
申请日：2021-10-29
Applicant: Business Objects Software Ltd.
Inventor： Paul O'Hara , Malte Christian Kaufmann , Esther Rodrigo Ortiz , Conor White
Abstract: 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：US11475021B2
公开(公告)日：2022-10-18
申请号：US16876441
申请日：2020-05-18
Applicant: BUSINESS OBJECTS SOFTWARE LTD.
Inventor： 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: G06F16/2457 , G06F16/2458
Abstract: 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：US20210365471A1
公开(公告)日：2021-11-25
申请号：US16877909
申请日：2020-05-19
Applicant: BUSINESS OBJECTS SOFTWARE LTD.
Inventor： Paul O'Hara , Robert McGrath , Ying Wu , Shekhar Chhabra , Eoin Goslin , Pat Connaughton , John Bowden , Alan Maher , David Hutchinson , Leanne Long , Malte Christian Kaufmann , Pukhraj Saxena , Priti Mulchandani , Anirban Banerjee
IPC: G06F16/26 , G06F16/28 , G06F16/2458
Abstract: The present disclosure involves systems, software, and computer implemented methods for generating insights based on numeric and categorical data. One example method includes receiving a request for an insight analysis for a dataset that includes at least one continuous feature and at least one categorical feature. Continuous features can have any value within a range of numerical values and categorical features are enumerated features that can have a value from a predefined set of values. A selection of a first continuous feature for analysis is received, and at least one categorical feature is identified for analysis. A deviation factor and a relationship factor are determined for each identified categorical feature. An insight score is determined for each identified categorical feature that combines the deviation factor and the relationship factor for the categorical feature. The insight score is provided for at least some of the identified categorical features.
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公开(公告)号：US11107166B2
公开(公告)日：2021-08-31
申请号：US16140760
申请日：2018-09-25
Applicant: BUSINESS OBJECTS SOFTWARE LTD.
Inventor： Paul O'Hara , Ying Wu
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, to predict future Day Sales Outstanding (DSO) forecasts for a number of future time periods. In one aspect, a method includes receiving open receivables financial line item data and revenue financial line item data, providing the open receivables financial line item data to a DSO predictor engine to generate a predicted open receivables that includes a multi-step time series forecasting regression generated from the open receivables financial line item data, providing the revenue financial line item data to the DSO predictor engine to generate a predicted revenue comprising the multi-step time series forecasting regression generated from the revenue financial line item data; generating a predicted DSO with the predicted open receivables and predicted revenue, and providing the predicted DSO to a client device.
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