LiveRamp. has been granted a patent for a method that enables the comparison of data graphs using a confusion matrix in a distributed environment. This process constructs a benchmark file from a source graph, allowing for entity resolution without transmitting personally identifiable information (PII). GlobalData’s report on LiveRamp gives a 360-degree view of the company including its patenting strategy. Buy the report here.
According to GlobalData’s company profile on LiveRamp, was a key innovation area identified from patents. LiveRamp's grant share as of June 2024 was 39%. Grant share is based on the ratio of number of grants to total number of patents.
Entity resolution comparison without transmitting personally identifiable information
The granted patent US12032630B2 outlines a method for comparing data graphs, focusing on the construction and evaluation of a benchmark file derived from a source data graph. The process begins with the creation of a benchmark file that includes multiple records, each representing an entity with a unique identifier known as a gold key. This benchmark file is then matched against an entity resolution system that utilizes a reference data graph. The method involves copying identifiers from matched entities in the reference graph to the benchmark file and generating an entity resolution confusion matrix. This matrix allows for a comprehensive comparison of the source data graph and the entity resolution system while ensuring that personally identifiable information (PII) remains protected.
The claims further detail the assignment of various types of gold keys, including person, household, and location keys, to enhance the uniqueness of entity representations. The identifiers in the reference identity graph are similarly categorized. The method also incorporates advanced computational techniques, such as distributed computing and parallel processing, to efficiently produce the confusion matrix. Additional steps include calculating total populations, total agreements, and disagreements between the benchmark file and the reference graph, as well as categorizing populations into various classifications based on their agreement status. This structured approach aims to facilitate accurate data comparison and analysis without compromising individual privacy.
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