Abstract: In one embodiment, a method receives, by a computing device, a text change in a text entry which includes one or more text units. The method may access, using a data store, a record associated with a user identifier which includes one or more top N similar hash values associated with the user identifier. The method may determine one or more hash values by applying a hash function over the one or more text units of the text change. The method may compare each of the one or more hash values to the one or more top N similar hash values. In response to determining a match between at least one of the one or more hash values and the one or more top N similar hash values, the method may determine a phrase suggestion using the text change to visually present on the computing device.

Abstract: In one embodiment, a method receives, by a computing device, a text change in a text entry which includes one or more text units. The method may access, using a data store, a record associated with a user identifier which includes one or more top N similar hash values associated with the user identifier. The method may determine one or more hash values by applying a hash function over the one or more text units of the text change. The method may compare each of the one or more hash values to the one or more top N similar hash values. In response to determining a match between at least one of the one or more hash values and the one or more top N similar hash values, the method may determine a phrase suggestion using the text change to visually present on the computing device.

Abstract: A computer-implemented process is programmed to programmatically receive at a first computer a digital electronic object including a source text that is directed to a target audience and that was composed at a second computer, determine a style value specifying a particular communication style from among different communication styles based on a communication profile of the target audience, evaluate the source text using a trained machine-learning model, receive a classification output from the machine-learning model, generate an output set of suggested edits to the source text based on comparing the classification output to the source text, and transmit the output set of suggested edits to the second computer. The classification output from the machine-learning model includes a modified text based on the source text. The modified text has been classified using the particular communication style specified in the style value.

Abstract: A computer-implemented process is programmed to programmatically receive at a first computer a digital electronic object including a source text that is directed to a target audience and that was composed at a second computer, determine a style value specifying a particular communication style from among different communication styles based on a communication profile of the target audience, evaluate the source text using a trained machine-learning model, receive a classification output from the machine-learning model, generate an output set of suggested edits to the source text based on comparing the classification output to the source text, and transmit the output set of suggested edits to the second computer. The classification output from the machine-learning model includes a modified text based on the source text. The modified text has been classified using the particular communication style specified in the style value.