What if you could observe the return back to normal function?

Warning: technical jargon ahead


Aging takes a long time. It’s like fragmention of a hard disc. The return to normal is like “defragmenting” the drive. It just works better.

How many ways might chronic pain, with its impairment of innate functions, accelerate fragmentation?

Could the back to normal functions reverse some of the effects of aging? Sensations resume their normal functions. Cold hands become normal, as in warm. It takes about as long as defragging a disc.


PhotoMed’s team of nerdy software and hardware engineers uncomfortably backed into these questions. We still don’t have answers after 20 years and self-funding of $20M. Machine learning (ML) techniques uncovered a few interesting features of anti-aging. It may be that ML works more efficiently with events in real time rather than it works with slow processes.


Unexpected outcomes

The team’s task was to develop PhotoMed’s quantum normalization therapy (QNT) for patients having “nothing worked” types of pain. Surgeries, medications, opioids, more surgeries, you get the picture. The patient’s disorders were not expected to improve because they had already tried everything for years. And nothing worked.

We chose some routine clips out of hundreds recorded by the Instant Verification System:


Reversing diabetic numbness to avoid wounds and amputations.


Ending persistent post-surgical pain and “phantom” pain.


The engineers hooked up sensors and cameras to their computers. The tools recorded events in real time without the bias of a hypothesis. Most of the time nothing happened. At least there were no side effects beside disappointment.

The events were unexpected by the patient, like hands warming after 30-years of coldness.

The engineers were happy because it was so easy to detect when the therapy “works”.

Find a partial-list here.


The anesthesiologist and neurologist advisors that they consulted were puzzled by the results. The outcomes appeared to be incompatible with current medical understanding and theories.


Could machine learning accelerate quantum normalization?

Silicon Valley thinking directed the engineer’s attention to things that happened quickly. The software engineers looked for binary outcome events. “0” or “1”. Did the patient return to normal, or not?

Before becoming PhotoMed’s engineers, they built precision hard and software tools for Intel before starting this search for fast, simple, and efficient answers. Andy Grove, from Intel, indoctrinated them that only the paranoid survive. Just seeing the too fast to understand responses with their own eyes meant nothing for their paranoid minds. So they built precision hard and software tools to document, in real time, what was going on and when.


PhotoMed’s team applied similar methods to developing PhotoMed’s quantum normalization therapy. Intel wanted to make rare defects even rarer, from 1-in-a million to 1-in-a billion. PhotoMed’s team wanted to make previously rare quantum normalization events common by customizing the therapy for the individual patient.

Self-funding and the engineering approach removed the pressure to quit when there appeared to be no short-term fix. No one dreamed that it would take 20 years. Especially quantum biology.

The team applied machine-learning techniques to examine the interaction between quantum photons and the return back to normal function. They quickly learned that the observed responses conflicted with the notion that pain will remain a long time. Their anesthesiologist, neurologist, and neurosurgeon advisors all found the responses puzzling because the responses and outcomes were too quick and were unpredicted by conventional explanations.

Machine learning recognized that the responses were not just masking pain. It seemed to be that a switch was being flipped. The improvements appeared to be deep in default systems. It was like that a hard disc was “defragged” and the operating systems went back-to-normal.

The team’s Silicon Valley roots demanded that the systems design consider the delivery cost and complexity for the user and the patient.


Without a hypothesis, it was time to be quiet. This wasn’t a science project. The task was to make the therapeutic algorithm more efficient.

Pain is the biggest contributor to premature aging. (1)

Many people have permanent “nothing worked” pain. They may have tried everything available. The attempts for relief may have introduced new sources of pain. 1-in-12 knee replacement patients wish that they did not have the operation. (2)

The “nothing worked” types of pain present a low probability of resolution, close to zero, using conventional interventions. The yield from the Triple 2 Algorithm is about 1-in-3.


The engineers were surprised when machining learning (ML) found what patients often remarked. They said, “I feel normal again”. ML also found that the “back to normal” function appears to be independent of life’s variables.


Less pain. Live longer. Live happier.




(1) https://www.apa.org/pubs/journals/releases/amp-a0035794.pdf Molton IR,Terrill AL, Overview of Persistent Pain in Older Adults, American Psychologist, Vol 69(2), Feb-Mar 2014, 197-207, DOI: 10.1037/a0035794

(2) https://www.ncbi.nlm.nih.gov/pubmed/14659522 , Harden RN, et al, Prospective examination of pain-related and psychological predictors of CRPS-like phenomena following total knee arthroplasty: a preliminary study, Pain. 2003 Dec;106(3):393-400. DOI: https://insights.ovid.com/article/00006396-200312000-00020