System and method for managing notifications of document modifications

A method of presenting information for one or more modification events associated with portions of an electronic content item, the method comprising:

• receiving, at a first time, a first user input indicating a selection of a first portion of the electronic content item by a first user, the first portion being smaller than an entirety of the electronic content item;
• determining that a first modification event, associated with a second user, occurred for at least a first sub-portion of the first portion prior to the first time;
• causing to be displayed, to the first user, in response to determining that the first modification event has occurred and receiving the first user input, a first user interface including a representation of the first modification event;
• causing to be displayed, to the first user, a second user interface including a first selectable preference for requesting notification of modification events for the electronic content item;
• receiving a second user input corresponding to a selection of the first selectable preference;
• determining that a second modification event, associated with a third user, occurred for at least a portion of the electronic content item at a second time subsequent to the first time;
• notifying the first user of an occurrence of the second modification event;
• receiving, at a third time subsequent to the second time, a third user input indicating a selection of the first portion of the electronic content item by the first user;
• determining that the second modification event, associated with the third user, occurred for at least a first sub-portion of the first portion during a period extending between the first time and the second time;
• causing to be displayed, to the first user, in response to determining that the second modification event has occurred and receiving the third user input, a second user interface including a representation of only the second modification event,
• preventing the first user interface, including the representation of the first modification event, from being displayed to the first user, in response to a determination that the first modification event has occurred but fails to include a high likelihood of being relevant to the first user, as determined by a machine learning model, and
• querying the first user as to whether the determination that the first modification event does not include a high likelihood of being relevant to the first user is correct, and, based upon an indication by the first user that the determination is not correct, allowing the first user interface to be displayed to the first user, and updating the machine learning model.

A data processing system for presenting information for one or more modification events associated an electronic content item, the system comprising:

• at least one processor;
• and
• one or more computer readable media including instructions which, when executed by the at least one processor, cause the at least one processor to:
• determine that a modification event, associated with a first user, occurred for at least a first portion of the electronic content item;
• determine the modification event is associated with a high likelihood of being relevant to a second user by analyzing the modification event using a machine learning model trained to predict a relevance of modification events to users, wherein the machine learning model is trained at least in part based on user interactions with the data processing system;
• cause to be displayed, to the second user, in response to the determination that the modification event has occurred and includes a high likelihood of being relevant to the second user, a first user interface including a representation of the modification event, and
• prevent from being displayed, to the second user, the first user interface including the representation of the modification event, in response to a determination that the modification event has occurred but fails to include the high likelihood of being relevant to the second user, as determined by the machine learning model,
• wherein the instructions, when executed by the at least one processor, cause the at least one processor to query the second user as to whether the determination that the modification event does not include a high likelihood of being relevant to the second user is correct, and, based upon an indication by the second user that the determination is not correct, allowing the first user interface to be displayed to the second user, and updating the machine learning model.

A data processing system for notifying a user of changes to specific portions of an electronic content identified as being of relevance to the user, the system comprising:

• at least one processor;
• and
• one or more computer readable media including instructions which, when executed by the at least one processor, cause the at least one processor to:
• receive an indication of the user accessing an electronic document;
• determine that one or more modification events have occurred in connection with the electronic content;
• present to the user, in response to the determination that one or more modification events have occurred, a user interface identifying each portion of the electronic document that is associated with the one or more modification events, wherein identifying the one or more modification events includes identifying modification events associated with the specific portions in a presentation format that differs from a default format used to identify other modification events, and wherein the user interface includes a summary of only the modification events associated with the specific portions and an indication of the relevance of each modification to the user, wherein the indication includes a confidence value indicative of an inferred relevance of the modification to the user, and
• prevent from being displayed, to the user, the user interface including a representation of the one or more modification events, in response to a determination that the one or more modification events have occurred but fail to include a high likelihood of being relevant to the user, as determined by a machine learning model,
• wherein the instructions, when executed by the at least one processor, cause the at least one processor to query the user as to whether the determination that the one or more modification events do not include a high likelihood of being relevant to the user is correct, and, based upon an indication by the user that the determination is not correct, allowing the user interface to be displayed to the user, and updating the machine learning model.