Snowflake had 15 patents in artificial intelligence during Q1 2024. Snowflake Inc’s patents filed in Q1 2024 cover a range of technologies including tuning machine learning operations, improving task scheduling on a cloud data platform, enhancing machine learning models with external data sources, combining NLP models for real-world document processing, and ensuring database privacy through differential privacy techniques using a hardware device. GlobalData’s report on Snowflake gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Snowflake grant share with artificial intelligence 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: Hyperparameter tuning in a database environment (Patent ID: US20240078220A1)
The patent filed by Snowflake Inc. describes a method for tuning a machine learning operation by generating hyperparameter sets based on input data, training multiple machine learning models, selecting the best model based on accuracy, and returning the output of the selected model in response to a data query. The hyperparameter sets are varied based on the volatility or range of the input data set, and may include factors like trend, seasonality, or holidays. The models are trained concurrently on multiple compute nodes and are focused on time series forecasting operations. The selection of the best model is based on comparing accuracy values of output data sets, with confidence intervals playing a role in the comparison.
The system and non-transitory computer-readable storage medium described in the patent implement the method outlined above. The system includes a processing device and memory to receive data queries, generate hyperparameter sets, train machine learning models, select the best model based on accuracy, and return the output. The processing device varies hyperparameter values based on data set characteristics, trains models on multiple compute nodes, and focuses on time series forecasting. The selection of the best model is based on comparing accuracy values, including confidence intervals. The non-transitory computer-readable storage medium contains instructions for executing the method, including generating hyperparameter sets, training models, selecting the best model based on accuracy, and returning the output in response to data queries.
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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.
