Hi everyone,
I’m exploring ideas around on-device analysis of user typing behavior on iPhone, and I’d love input from others who’ve worked in this area or thought about similar problems.
Conceptually, I’m interested in things like:
- High-level sentiment or tone inferred from what a user types over time using ML-models
- Identifying a user’s most important or frequent topics over a recent window (e.g., “last week”)
- Aggregated insights rather than raw text (privacy-preserving summaries: e.g., your typo-rate by hour to infer highly efficient time slots or "take-a-break" warning typing errors increase)
I understand the significant privacy restrictions around keyboard input on iOS, especially for third-party keyboards and system text fields. I’m not trying to bypass those constraints—rather, I’m curious about what’s realistically possible within Apple’s frameworks and policies. (For instance, Grammarly as a correction tool includes some information about tone)
Questions I’m thinking through:
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Are there any recommended approaches for on-device text analysis that don’t rely on capturing raw keystrokes?
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Has anyone used NLP / Core ML / Natural Language successfully for similar summarization or sentiment tasks, scoped only to user-explicit input?
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For custom keyboards, what kinds of derived or transient signals (if any) are acceptable to process and summarize locally?
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Any design patterns that balance usefulness with Apple’s privacy expectations?
If you’ve built something adjacent—journaling, writing analytics, well-being apps, etc.—I’d appreciate hearing what worked, what didn’t, and what Apple reviewers were comfortable with.
Thanks in advance for any ideas or references 🙏
