7. Conclusions
Conclusions
Digitally identifying an individual as a movement pattern (trend of motion-alteration) in a time-cached and fixed digital geo-location offers significant improvements in data-analytical perspective. It does this by offering changes in externally observable behaviour (e.g. gait) as a real-world link to other data representing other individuals as a “behavioural pattern” and represented however indirectly within the data network representing the feelings and sentiments of their experience of the world.
It does so whilst offering perfect levels of control and high levels of supervision from the individual user.
Doing so offers organisations with data the opportunity to use AI tools and neural networks to identify patterns that may be user-beneficial and sell these back to users as part of an intellectual-property distribution platform in return for an improved customer experience. The latter being a commercially valuable commodity makes it an interesting by-product for companies looking to manage the investment-risk profile associated with this project.
This process can be managed through blockchain technology and would not require any new regulation not already in place in that sphere. An industry around curating such access would logically emerge to capitalise on sensory experiences, art, intellectual property, intelligence research and good product/user-interface design. Ultimately capitalising on enabling others to benefit from their life-choices and experiences as rendered in their data profile.
This proposal requires relatively little investment and has much opportunity to rapidly demonstrate significant value. The development of a user-avatar capture platform would involve a few cameras and the ability to acquire personal data. It would also require building a method to access other databases with directed questions about the meaning of each of the traits acquired and the holding of patterns identified, aka “archetypes” until confidence has been established and allocated within the archetype. Other industries are already in full swing with this process, including the legal industry. Healthcare however has not, due to privacy concerns. By undertaking this step forward, as it has been done in so many other fields before (but now in healthcare), the concept of “archetypes” can be used to access other and vast data-fields like psychology, sociology, education, and criminology, and significantly improve the wider user-experience. It is hardly surprising yet poetic then, seeing this field’s data has such an intimate relation with the unique individual personal life-experience, that the location from which such superior computation would become possible was to be healthcare.
Each individual organisation would just continue to make such archetypical avatars of their principal client (s) (to whichever degree they can viably individualise and customise), the individual (Patient X) as they already do. In this paper, we suggest that by creating one associated with their movement pattern, it can be associated with their well-being, and it, in turn, to their treatment, habits, diets and life experiences. By association to a clinical body of knowledge with strong links to reliable assumptions regarding mental state, dietary habits or physical traits as well as predictors associated to API’s, this computational “over-reach” offers an exponentially intimate and meaningful archetype from which to compute future probabilities as experienced by others most-like them.
This computational model represents a real-world Bell inequality violation experiment and would demonstrate that:
a) Reality is only quantum once it has been observed
b) Data describing reality is entangled.
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Acknowledgments
The authors have no conflicts of interest to declare and thank all collaborators and supporters listed and unlisted.
