Siemens had 44 patents in big data during Q1 2024. Siemens AG filed patents in Q1 2024 for methods and systems related to detecting data anomalies, medical data analysis, radiological visualization data, and radio performance diagnostics. These inventions include methods for flagging anomalous test data, generating data analysis tools for medical images, comparing anatomical positions from sensor data and diagnostic reports, providing radiological visualization data based on confidence scores, and diagnosing radio performance changes in transceiver nodes. GlobalData’s report on Siemens gives a 360-degree view of the company including its patenting strategy. Buy the report here.

Siemens grant share with big data as a theme is 22% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Method, apparatus and electronic device for detecting data anomalies, and readable storage medium (Patent ID: US20240104072A1)

The patent filed by Siemens AG describes a method and apparatus for detecting data anomalies by matching test data with a data rule based on historical data. The data rule consists of antecedent and consequent predicate sets, where the test data is flagged as anomalous if it satisfies all antecedent predicates but fails to satisfy at least one consequent predicate. The method involves determining global predicate sets, dividing them into antecedent and consequent sets, and screening out rules that meet specific constraint conditions. Additionally, the apparatus includes modules for receiving test data, matching with data rules, and screening out rules based on constraints.

Furthermore, the method and apparatus are designed to handle historical data containing discrete and continuous variables. For discrete variables, candidate predicates are generated based on possible values, while for continuous variables, decision tree models are used to train and extract cut-off values for creating predicate sets. The apparatus includes modules for generating candidate predicates, traversing discrete and continuous variables, training decision tree models, and creating predicate sets based on sorting results. Overall, the patent outlines a comprehensive approach to detecting data anomalies using historical data and specific rule-based criteria.

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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.