There are other machine learning systems that will need to be trained on each user's behavior for them to work correctly, Kundu said. The way users search is one such example: Every person uses different methods to search, and it will be necessary to train the system on the way a particular user works in order to be useful.
Note data submitted for those purposes would have the identity of the user removed, and the data scientists analyzing it would see aggregated data, not a specific whole note from a user.
"That was always the intention," Kundu said. "We respect what you put into Evernote too much to think of it in any way besides that."
"Our primary goal with the communication was to be as blatantly transparent as we possibly could, and in the spirit of not trying to get to nuance on it, we just went for the blunt instrument," said Andrew Malcom, Evernote's senior vice president of marketing.
Those provisions -- especially responding to law enforcement requests -- are common among other online service providers like Evernote. Apple, Microsoft, and Google, which all offer competing services, also provide data from users' accounts in response to police requests they deem appropriate.
In some ways, Evernote's willingness to discuss these issues publicly is a differentiator from its competition.
"The nuanced, every-single-word-is-massaged privacy policies that are overly vague, that intentionally introduce gray areas, that are commonplace among tech companies -- that's not our style," O'Neill said.
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