Zuora’s patent involves a computing server processing data from various sources within a domain, extracting objects from unstructured data, and using machine learning to generate activity time sequences and process models. The method identifies outliers and bottlenecks in processes based on defined metrics, enhancing transaction analysis with domain knowledge ontology. GlobalData’s report on Zuora gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Zuora, Social media analytics was a key innovation area identified from patents. Zuora's grant share as of May 2024 was 42%. Grant share is based on the ratio of number of grants to total number of patents.
Data processing server for domain data extraction and analysis
A recently granted patent (Publication Number: US12001469B2) outlines a computer-implemented method for analyzing processes within a domain. The method involves receiving master data and transaction data schemas, identifying named entities in unstructured data based on these schemas, associating activities with these entities, labeling activities with timestamps to create an activity time sequence, generating event sequences related to a target process, combining these sequences to form a process model represented as a directed and branched graph, and identifying bottlenecks in the process based on performance indices and deviations from average values. The method also includes augmenting the process model with domain knowledge ontology, utilizing machine learning models like long short term memory neural networks to generate activity time sequences, and employing recurrent neural networks to predict the next steps in the process.
Furthermore, the patent describes a system and a non-transitory computer-readable medium for implementing the method. The system includes a computing server with memory and processors to execute instructions for identifying entities, activities, generating event sequences, creating process models, and identifying bottlenecks. The instructions also cover augmenting the process model with domain knowledge ontology, classifying transactions, determining outliers, and constructing process variants based on transaction signatures. The computer-readable medium stores code for executing the method steps, including identifying bottlenecks based on performance indices, classifying process models into steps, and marking steps associated with deviations as bottlenecks. Overall, the patent presents a comprehensive approach to process analysis within a domain, utilizing advanced technologies like machine learning and neural networks to enhance efficiency and accuracy in bottleneck identification and process optimization.
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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.
