Microsoft International Holdings B.v.

Boris I Levin

A computer-implemented method for processing data includes receiving an input from a user including positive examples and negative examples of a specified data type. The positive examples include first character strings that belong to the specified data type, and the negative examples include second character strings that do not belong to the specified data type. The first and second character strings are processed to create a set of attributes that characterize the positive examples. A decision tree is built, based on the attributes, which when applied to the first and second strings, distinguishes the positive examples from the negative examples. The decision tree is applied to the data so as to identify occurrences of the specified data type.

Detection of patterns in data records

A method for processing information includes receiving a set of records, which include a plurality of fields containing data regarding respective items, and selecting a field that occurs in all of the records and contains multiple terms in each of the records. At least first and second terms that occur among the terms in the selected field in the records are identified, such that the records are partitioned into at least first and second respective subsets by occurrences of the at least first and second terms in the selected field. Responsively to partitioning of the records by the occurrences, it is determined that the at least first and second terms correspond to at least first and second different values of an attribute of the items. The data are classified according to the values of the attribute.

Detection of attributes in unstructured data

A computer-implemented method for processing data includes receiving an initial set of records including terms describing respective items in specified categories. Based on the initial set of records, respective term weights are calculated for at least some of the terms with respect to at least some of the categories. Each term weight indicates, for a given term and a given category, a likelihood that a record containing the given term belongs to the given category. Upon receiving a new record, not included in the initial set, respective assignment metrics are computed for two or more of the categories using the respective term weights of the particular terms in the new record with respect to the two or more of the categories. The new record is classified in one of the two or more of the categories responsively to the assignment metrics.

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Rishon Le-Zion, Israel

Boris I Levin