Envestnet. has been granted a patent for a hierarchical classification system that processes transaction records using cluster-level and cluster-specific classifiers. This system predicts clusters and labels of interest for input records, enhancing data organization and retrieval for financial transactions. GlobalData’s report on Envestnet gives a 360-degree view of the company including its patenting strategy. Buy the report here.
Smarter leaders trust GlobalData
According to GlobalData’s company profile on Envestnet, was a key innovation area identified from patents. Envestnet's grant share as of July 2024 was 39%. Grant share is based on the ratio of number of grants to total number of patents.
Hierarchical classification of transaction records using cluster-specific classifiers
The patent US12061629B2 outlines a method and system for processing transaction records using a cluster-level classifier. The method begins with receiving a transaction record and processing it to predict its belonging to a specific cluster among multiple clusters. This prediction is based on a classifier trained with labeled transaction records, which include tag-level sequence data derived from token sequences and associated tags representing various aspects of the records. The clustering process involves analyzing distances and the ordering of tags within the tag sequences. Once the appropriate cluster is identified, a corresponding cluster-specific classifier is utilized to predict relevant labels of interest, which characterize specific attributes of the transaction records. The final output is a representation of the transaction record linked to the predicted label, intended for storage or further data processing.
Additionally, the patent specifies that the method can involve various techniques, such as computing Levenshtein distances for clustering and employing character-level Recurrent Neural Networks (RNNs) for training classifiers. The tags can represent diverse aspects of the transaction, including merchant names, card numbers, and transaction locations. The system is designed to enhance the classification and labeling of transaction records, thereby improving data management and processing efficiency. The claims also highlight the potential for the transaction record to include descriptions from service provider computers, detailing financial transactions between parties. Overall, the patent presents a structured approach to transaction record classification and labeling, leveraging advanced machine learning techniques.
To know more about GlobalData’s detailed insights on Envestnet, buy the report here.
Data Insights
From
The gold standard of business intelligence.
Blending expert knowledge with cutting-edge technology, GlobalData’s unrivalled proprietary data will enable you to decode what’s happening in your market. You can make better informed decisions and gain a future-proof advantage over your competitors.
GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.
GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
