Koninklijke Philips had one patents in edge computing during Q4 2023. The patent filed by Koninklijke Philips NV in Q4 2023 is for a federated learning system where a federated model is trained on local training datasets of multiple edge devices. Each edge device obtains the current federated model, determines a model update based on its local dataset, and sends out the update. The model update is determined by applying the current model to training inputs, filtering inputs that do not match expected outputs, and training the model only on the filtered inputs. GlobalData’s report on Koninklijke Philips gives a 360-degreee view of the company including its patenting strategy. Buy the report here.
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Koninklijke Philips grant share with edge computing as a theme is 0% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Federated learning (Patent ID: US20230394320A1)
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The patent filed by Koninklijke Philips NV describes a federated learning system where a federated model is trained on local training datasets of multiple edge devices. In each iteration, an edge device obtains the current federated model, determines a model update based on its local training dataset, and sends the update to other devices. The model update is determined by applying the current model to a training input, including the input in a subset if the output does not match the expected output, and training the model on only the filtered inputs.
The system allows for iterative training of the federated model, with some iterations using the full local training dataset and others using only a subset of filtered inputs. The edge device can be an IoT device and is capable of training the model through multiple epochs on the filtered inputs. Additionally, the system includes mechanisms for determining confidence scores, updating model parameters, and aggregating model updates from multiple devices. The aggregation device in the system is responsible for sending the current model to edge devices, receiving updates, and aggregating them to update the federated model.
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