Abstract: A portable electronic device for instant messaging is disclosed. One aspect of the invention involves a graphical user interface (GUI) on a portable electronic device with a touch screen display. The GUI has a set of messages exchanged between a user of the device and another person. The set of messages are displayed in a chronological order. In response to detecting a scrolling gesture comprising a substantially vertical movement of a user contact with the touch screen display, the display of messages are scrolled in accordance with a direction of the scrolling gesture. The detecting of the scrolling gesture is substantially independent of a horizontal position of the user contact with the touch screen display.

Abstract: In some embodiments, a method can include executing a first model to extract a first region of interest (ROI) image and a second ROI image from an image that shows an item and an indication of information associated to the item. The first ROI image can include a portion of the image showing the item and the second ROI image can include a portion of the image showing the indication of information. The method can further include executing a second model to identify the item from the first ROI image and generate a representation of the item. The method can further include executing a third model to read the indication of information associated to the item from the second ROI image and generate a representation of information.

Abstract: Disclosed are systems and methods including starting with a first number of images, generating a second number of images by digital operations on the first number of images, extracting features from the second number of images, and generating a classification model by training a neural network on the second number of images wherein the classification model provides a percentage likelihood of an image's categorisation, embedding the classification model in a processor and receiving an image for categorisation, wherein the processor is in communication with a POS system, the processor running the classification model to provide output to the POS system of a percentage likelihood of the image's categorisation.

Abstract: In some embodiments, a method can include executing a first model to extract a first region of interest (ROI) image and a second ROI image from an image that shows an item and an indication of information associated to the item. The first ROI image can include a portion of the image showing the item and the second ROI image can include a portion of the image showing the indication of information. The method can further include executing a second model to identify the item from the first ROI image and generate a representation of the item. The method can further include executing a third model to read the indication of information associated to the item from the second ROI image and generate a representation of information.

Abstract: Disclosed are systems and methods including starting with a first number of images, generating a second number of images by digital operations on the first number of images, extracting features from the second number of images, and generating a classification model by training a neural network on the second number of images wherein the classification model provides a percentage likelihood of an image's categorisation, embedding the classification model in a processor and receiving an image for categorisation, wherein the processor is in communication with a POS system, the processor running the classification model to provide output to the POS system of a percentage likelihood of the image's categorisation.