SAP had 20 patents in big data during Q1 2024. SAP SE’s patents filed in Q1 2024 cover a range of technologies, including defining and deploying distributed neural networks, creating an artificial intelligence work center for ERP data, sharing data assets between customers onboarded at different data centers, in-memory computing systems for transaction and analytical processing, and defining table user-defined functions in a graph query language for executing queries on graph data stored in a database. GlobalData’s report on SAP gives a 360-degree view of the company including its patenting strategy. Buy the report here.
SAP grant share with big data as a theme is 60% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Distributed neural network (Patent ID: US20240104359A1)
The patent filed by SAP SE introduces techniques for defining, deploying, and utilizing distributed neural networks consisting of various computing elements, including IoT devices. The distributed neural network assigns specific neurons to individual computing elements, managing data processing requests even in the absence of certain computing elements. The configuration information includes defining input providers, weights, output receivers, and activation functions for each computing element acting as a neuron.
The patent claims detail a computing system that receives a neural network definition, assigns computing elements as neurons, and sends information to these elements for processing data requests. The system can handle scenarios where computing elements are unreachable by reassigning neurons to other available elements. Additionally, the patent describes methods for processing data requests using distributed neural networks, where IoT devices with hardware sensors can be utilized as computing elements. The patent also covers scenarios where certain computing elements are unavailable, allowing data processing to continue based on configuration information. Each node in the distributed neural network is implemented on different computing elements, ensuring redundancy and fault tolerance in the system.
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