# Christoph Lingenfelder, Herrenberg DE

## Christoph Lingenfelder, Herrenberg DE

Patent application number | Description | Published |
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20090182554 | TEXT ANALYSIS METHOD - A list of reference terms can be provided. Text and the list of reference terms can be broken down into tokens. At least one candidate can be generated in the text for mapping to at least one of the reference terms. Characters of the candidate can be compared to characters of the reference term according to one or more mapping rules. A confidence value of the mapping can be generated based on the comparison of characters. Candidates can be ranked according to their confidence value. | 07-16-2009 |

20120084251 | PROBABILISTIC DATA MINING MODEL COMPARISON - A first data mining model and a second data mining model are compared. A first data mining model M | 04-05-2012 |

20120158624 | PREDICTIVE MODELING - A predictive analysis generates a predictive model (Padj(Y|X)) based on two separate pieces of information, | 06-21-2012 |

20130144833 | PROCESSING DATA IN A DATA WAREHOUSE - Data of a database environment, which includes hierarchy information and a matrix of values, is processed. The hierarchy information includes at least two sets of identification codes and defines at least two groups of identification codes. The matrix of values includes at least two columns of identification values. At least one simple filter object is generated based on a user input. Each simple filter object defines an ad hoc group of identification codes selected from a respective one of the sets of identification codes. A filtered operation object that specifies an operation and at least one of the simple filter objects is generated based on a user input. Each of the ad hoc groups differs from each of the groups defined by the hierarchy information. | 06-06-2013 |

20140180973 | Iterative Active Feature Extraction - Techniques for iterative feature extraction using domain knowledge are provided. In one aspect, a method for feature extraction is provided. The method includes the following steps. At least one query to predict at least one future value of a given value series based on a statistical model is received. At least two predictions of the future value are produced fulfilling at least the properties of 1) each being as probable as possible given the statistical model and 2) being mutually divert (in terms of numerical distance measure). A user is queried to select one of the predictions. The user may be queried for textual annotations for the predictions. The annotations may be used to identify additional covariates to create an extended set of covariates. The extended set of covariates may be used to improve the accuracy of the statistical model. | 06-26-2014 |

20140180992 | Iterative Active Feature Extraction - Techniques for iterative feature extraction using domain knowledge are provided. In one aspect, a method for feature extraction is provided. The method includes the following steps. At least one query to predict at least one future value of a given value series based on a statistical model is received. At least two predictions of the future value are produced fulfilling at least the properties of 1) each being as probable as possible given the statistical model and 2) being mutually divert (in terms of numerical distance measure). A user is queried to select one of the predictions. The user may be queried for textual annotations for the predictions. The annotations may be used to identify additional covariates to create an extended set of covariates. The extended set of covariates may be used to improve the accuracy of the statistical model. | 06-26-2014 |

20140258311 | INSIGHT DETERMINATION AND EXPLANATION IN MULTI-DIMENSIONAL DATA SETS - Techniques are disclosed for determining reasons underlying insights gleaned from multi-dimensional data. In one embodiment, a contingency table is accessed that represents multiple dimensions of the data, in order to identify one or more insights. One or more dimensions, other than the represented dimensions, are evaluated to identify one or more reasons underlying a first insight of the one or more insights, and the one or more reasons are output. | 09-11-2014 |

20140258312 | INSIGHT DETERMINATION AND EXPLANATION IN MULTI-DIMENSIONAL DATA SETS - Techniques are disclosed for determining reasons underlying insights gleaned from multi-dimensional data. In one embodiment, a contingency table is accessed that represents multiple dimensions of the data, in order to identify one or more insights. One or more dimensions, other than the represented dimensions, are evaluated to identify one or more reasons underlying a first insight of the one or more insights, and the one or more reasons are output. | 09-11-2014 |