Panjiva, Inc.

Jason Seth Prentice

James Ryan Psota

Peter Goodings Swartz

Timothy George Garnett

Giulio Gueltrini

A signal relationship is defined between a granular data value and a target data value. At least a portion of the granular data value corresponds to a granular latency value that is smaller than a target data latency value corresponding to the target data value. Granular data corresponding to the granular data value is interpreted. The granular data is aggregated in response to the signal relationship. A value of the target data value for a selected time reference is estimated, and the estimated value of the target data value is provided as a nowcasting prediction of the target data value.

Nowcasting abstracted census from individual customs transaction records

An apparatus includes a data access circuit that interprets data records, each having a number of data fields, a record parsing circuit that determines a number of n-grams from terms of each of the data records and maps the number of n-grams to a corresponding number of mathematical vectors, and a record association circuit that determines whether a similarity value between a first mathematical vector for the first data record and a second mathematical vector for the second data record is greater than a threshold similarity value, and associates the first and second data records in response to the similarity value exceeding the threshold similarity value. An example apparatus includes a reporting circuit that provides a catalog entity identifier, associates each of the first term and the second term to the catalog entity identifier, and provides a summary of activity for an entity.

Natural language processing for entity resolution

The technology features a system and computer-implemented method for resolving a relationship between objects. A target object index is generated based on a group of target objects. One or more lookup operations is performed on each target object in the target object index for each source object in a group of source objects. A plurality of source target object pairs is generated, each source target object pair comprising one source object and one target object having at least one matching data value. Each source target object pair is converted into a numeric feature vector. The numeric feature vector is classified corresponding to each source target object pair using a binary classifier. A match score to each source target object pair is applied based on the classification using the binary classifier. Any source target object pair having a match score lower than a match threshold value is discarded.

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Arlington, MA, United States of America

Jason Seth Prentice