<table><tr><th>From:</th><th>jeffrey E. &lt;jeevacation@gmail.com&gt;</th></tr><tr><td>Sent:</td><td>Thursday, November 2, 2017 10:26 PM</td></tr><tr><td>To:</td><td>Steven Sinofsky</td></tr><tr><td>Subject:</td><td>Re:</td></tr></table>

thx

On Thu, Nov 2, 2017 at 6:24 PM, Steven Sinofsky <██████████████████████████████████████████> wrote:

Greetings Masha,

I apolo=ize for the delay. I was at some events in in UK where I did not hav= connectivity.

I have worked on a related portfol=o company so I will have to be a bit abstract on the specifics of this opp=rtunity. So please excuse these brief thoughts. Also, the only infor=ation I had was what was provided in your mail which was limited relative =o any detailed analysis.

* In general the challen=e with glucose measurement is the finger prick. Any device that relies on =his will only be marginally better than any other device, whether or not t=ere is software or a slightly more convenient measuring device. This is a =tatement about the inconvenience of a prick (and long term challenges) but=also the medical challenges on relying on that point in time.

* =y sense is that going down the path of an innovation, that still has a pri=k, but requires a level of FDA approval is a difficult one to approach.

" This is a very crowded space. There are a lot of apps, a lot of m=asuring devices, a lot of mixtures of app and measuring devices. It is ver= difficult to avoid appearing as a commodity to consumers.

* Whi=e I understand there is potential to see innovation using novel approaches=to analysis of data, it is not clear to me how much better the approach ca= be for an individual with data.

* The real opportunity I might =ee is around measuring glucose or some related telemetry to assist in comp=iance that is outside the scope of a finger prick and measuring glucose di=ectly. It seems like we should have some other data point upon which to ap=ly machine learning.

I hope this helps...any frie=d of JE is a friend of mine.

On Nov 1, 2017, at 4:43 PM, Mas=a Drokova ██████████████████████████████████████████ >>
wrote:

https://drive.google.com/file/d/0B5O93D3IJArEbXBsdlI4elV=b0U/view

Our memo:

https://docs.google.com/document/d/1M7ZV7VEl8CTnb4EtYWFH2sq5dc=m0PM6g1AlcwrlOtM
<https://docs.google.c=m/document/d/1M7ZV7VEl8CTnb4EtYWFH2sq5dcCm0PM6g1AlcwrlOtM>

Center Health is bu=lding an Al-based glucose monitoring system for the 1 in 11 Americans=who suffer from diabetes, based on machine learning and their personalized=Al, Aria. Users subscribe to their disposable test strips, a $148/yr =S market, which are delivered monthly, as Aria learns about their diabetes=and prompts behavioral changes to lower blood sugar. The system =s an order of magnitude cheaper than existing technologies, premised on le=eraging data to help users see what daily elements are affecting thei= blood sugar, and predicting dangerous highs and lows before they happen..C2.

## Pros

— Existing gluco=eters from big companies are very old-fashioned and outdated, those compan=es make their revenue from overpriced strips

— Direc= competitors, such as i Health and Dario have negative customer=reviews and minor

— Uses FDA-approved circuits to get the ap=roval in an automated manner

## Cons

— Enormous pressure both from the industry players an= companies such as Apple and Google that try to develop non-invasive gluco=e monitoring that will wipe out test strip products

Q=94 Hardware startup without a product to sell yet, finalizing the developm=nt — Young team

</=iv>

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