Cloudmind line-item tracking and credit-assignation
Line-item tracking could be implemented in the idea-rich brainstorming environment of cloudminds by using secure deep-learning algorithms to record all participant activity.
One current feature of deep-learning systems is the capture and output of activity in interchangeable formats, accommodating audio, video, and text formats.
Each participant’s most minute thought formulations might be recorded with a neural feed and a time-date stamp that is logged to a blockchain.
This would be similar to using a deep-learning algorithm to automatically transcribe Skype audio calls into writing, and posting the result to a blockchain to validate ownership.
Important steps in the brainstorming process might thus be tracked, for example whether multiple parties have the same idea simultaneously.
There could be additional benefits as well, for example, in the area of novel discovery, elucidating the brainstorming process itself and identifying the precursor factors to actual ideas.
Blockchain cloudmind tracking would be conceptually similar to applying a software version control system (such as Github, SVN, or CVS); this is literally a line-item tracking system for a brainstorming session.
This kind of feature could permit even more precise assignment of credit in cloudmind collaborations.
Further, BCI cloudminds might facilitate brainstorming with new functionality such as recognizing and consolidating generative threads and posing them back to participants.