Adobe, Inc.

Pin Zhang

Hiroyuki Hayashi

Chhaya Niyati Himanshu

Messages are processed to generate effectiveness predictions and/or other insights associated with the messages. Candidate messages are processed through a natural language processing (NLP) component to parse the candidate message into message elements for further processing. The message elements are converted to a vector or set of vectors, which are provided as input to a machine learning model to make predictions of message effectiveness. A contribution score can be made for each message element of the candidate message, which may be indicative of the importance or relevance for the individual message element to the overall predicted message effectiveness. Other message elements not originally within the message can be provided as candidates to replace message elements already located within the message. In this way, a message that is likely to be effective, such being likely to have a high conversion rate, can be published or otherwise distributed.

Generating message effectiveness predictions and insights

This disclosure involves the automatic sizing and placement of text within an image background. For example, a computing system obtains reference font size information for a font type to be applied to message text for display on a digital image. The computing system detects, within an image background of the digital image, a target region having proportions that enclose the message text based on the reference font size information. The computing system determines a target font size for the message text. The target font size allows the message text, when rendered in the font type at the target font size, to fit within the target region of the image background. The computing system generates a combined digital image by rendering the message text in the font type at the target font size within the target region of the image background.

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Kanagawa, Japan

Pin Zhang