Inventec had 18 patents in artificial intelligence during Q4 2023. The Inventec Corp patents filed in Q4 2023 include a federated learning method using data digest, an image data augmentation device for object detection, a fan control method using neural networks, a value-added content providing method for pop-up windows, and a data splitting method for validating machine learning on BP datasets. These patents cover a range of technologies including machine learning, image processing, fan control, and data analysis. GlobalData’s report on Inventec gives a 360-degreee view of the company including its patenting strategy. Buy the report here.

Inventec grant share with artificial intelligence as a theme is 16% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Fedrated learning system and method using data digest (Patent ID: US20230409965A1)

The patent filed by Inventec Corp. describes a federated learning method using data digest, where a general model is sent to multiple client devices by a moderator. Each client device generates encoded features from raw data and performs a training procedure, including updating the general model, computing feature and label weighted sums, and sending digests to the moderator. The moderator determines absent and present clients, generates replacement and aggregation models, and trains the aggregation model to update the general model based on a moderator loss function.

The system and method outlined in the patent involve a complex process where client devices generate encoded features, update models, and send digests to a moderator for aggregation and training. The method includes selecting features and labels, determining absent and present clients, and adjusting model weights based on loss functions. The system comprises client devices with processors for feature extraction and communication circuits, as well as a moderator with processors for model generation, aggregation, and training. This federated learning approach using data digest aims to improve model training efficiency and accuracy by distributing the process among multiple devices and utilizing digests for communication and aggregation.

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