MyFiziq Limited updated its shareholders with further performance and data protection enhancements to MYQ's technology. The new update from MyFiziq will see it move its patented segmentation model from Amazon's Web Services platform to Apple's iOS device platform. The software update allows users to run core functionality directly in their device and addresses a number of global requirements around transfer and storage of personal data. Currently the MyFiziq measurement process sends encrypted images to the cloud for processing. The new Image Capture Process SDK will instead use an in device embedded machine learning model to create non-personally identifiable segmented images for processing. This change allows anonymity to be protected as photos taken never need to leave a user's device. The new functionality will allow partners to further demonstrate accountability and compliance with data regulations across various regions. In additional to data privacy benefits, in device image segmentation also enables other technical and cost saving advantages. The output of the segmentation process is substantially reduced and compresses the current upload/return time by 95%. This in-turn allows for multiple attempts to be packaged in single classification object. At approximately 15 seconds per attempt, the end-to-end process is also 4 times faster than the previous average compute of 60 seconds. Combined, this effectively increases MyFiziq's throughput capacity 32-fold and results in massive cost reductions for both compute and storage. The new model utilises Apple's Metal framework (CoreML) and is part of MyFiziq's ongoing goal to reduce cloud compute time and cost whilst maintaining accuracy and repeatability. The update will allow MyFiziq to process up to 4,800,000 avatar requests an hour without any change to the current Amazon Web Services platform infrastructure MyFiziq has built. An Android version via a TensorFlow model is currently under development and nearing completion.