Training and/or utilizing an interaction prediction model to determine when to interact, and/or prompt for interaction, with an application on the basis of an electronic communication

A method, comprising:

• identifying, by a client device, an electronic communication,
• the electronic communication including content that comprises natural language content, and
• the electronic communication being received at the client device via a network interface of the client device, or being formulated at the client device via at least one user interface input device of the client device;
• determining, by the client device, whether any interaction occurs with a second application on the basis of the electronic communication,
• wherein the second application is in addition to an electronic communication application via which the electronic communication is formulated, or via which the electronic communication is rendered after being received;
• updating, by the client device, trained parameters of at least part of a trained machine learning model based on whether any interaction occurred with the second application on the basis of the electronic communication, wherein updating the trained parameters comprises:
• determining a gradient based on whether any interaction occurred with the second application on the basis of the electronic communication, and based on a prediction value generated based on processing of features, of the electronic communication, using the trained machine learning model, and
• backpropagating the gradient over at least a portion of the trained machine learning model;
• and
• using, by the client device, the trained machine learning model with the updated trained parameters in determining whether to present an additional selectable interface element for an additional electronic communication received or formulated at the client device.

A method performed by one or more processors of a client device, comprising:

• identifying, by a client device, an electronic communication,
• the electronic communication including content that comprises natural language content, and
• the electronic communication being received at the client device via a network interface of the client device, or being formulated at the client device via at least one user interface input device of the client device;
• applying, by the client device, as at least part of input to a trained machine learning model that is stored locally at the client device:
• tokens of the natural language content of the electronic communication;
• processing, by the client device, the input using the trained machine learning model to generate a predicted interaction value,
• the predicted interaction value indicating a likelihood of interaction with a second application, accessible to the client device, on the basis of the electronic communication,
• wherein the second application is in addition to an electronic communication application via which the electronic communication is formulated, or via which the electronic communication is rendered after being received;
• determining, by the client device based on the predicted interaction value, to present a selectable interface element via a display of the client device;
• generating, based on processing of the tokens of the natural language content of the electronic communication using a separate trained machine learning model, at least part of content of the electronic communication to provide to the second application, wherein the at least part of the content is a subset of the content of the electronic communication;
• based on the at least part of the content being generated using the separate trained machine learning model:
• causing selection of the selectable interface element to cause the interaction with the second application to provide the at least part of the content to the second application;
• and
• in response to selection of the selectable interface element:
• interfacing with the second application to provide at least part of the content of the electronic communication to the second application.