Health, Wellbeing and Companionship

Health & Wellness Agents With Longitudinal Memory

Health chatbots and apps don’t talk to each other and neither do they have access to the user's medical history. A user can create a “health” context using the OCL which stores:

• Medication history

• Fitness data (either manually inputted or through health wearables)

• Therapy notes or mood logs

In this way, the user can interact with multiple agents (e.g., a fitness bot, a mental health coach, a telemedicine app), and connect the same context to them so all of them reference the same shared memory securely, enabling continuity and full understanding in care and advice.

Lifelong Learning Companion

Educational platforms don't remember what you’ve already learned or struggled with. But an AI tutor that has persistent context can keep a track of your learning history across platforms and agents:

• Tracks your pace, knowledge gaps, preferred learning formats

• Adjusts difficulty accordingly

• Follows you across apps and agents

Over time, your personalized “learning context” grows and improves the relevance of every new lesson or quiz.

Digital Companions and AI Friends

AI companions today lack privacy-preserving long term memory and emotional continuity.

With OCL, you can create a context that remembers your life story, emotional triggers, favorite metaphors, and relationship dynamics. When you move platforms or apps, the emotional memory comes with you.

Posthumous Agents of Your Loved Ones

Talking to your loved ones from beyond your grave by transferring your memory to AI has always been a use case shown in science fiction. However, with the recent advancements in AI, this might become a reality in the upcoming decades. There already are companies working on making this vision a reality.

If this is something that people want, we believe that the context and memory of the person should be stored on a neutral layer that has long-term persistence, resistance from malicious use, and where the access control of the context is done in a transparent way.

OCL is a perfect candidate for these use cases.