<table><tr><th>From:</th><th>jeffrey E. &lt;jeevacation@gmail.com&gt;</th></tr><tr><td>Sent:</td><td>Friday, November 3, 2017 4:53 AM</td></tr><tr><td>To:</td><td>Masha Drokova</td></tr><tr><td>Subject:</td><td>Re:</td></tr></table>

He is not an investor type . He is s partner at a fi=m but used to be at Microsoft. Just use him for advice

On Thu, Nov 2, 2017 at 11:13 PM Masha Drokova
>=wrote:

Thank you for the comprehensive feedback Steven.

For me the main value is =hat the system is an order of magnitude cheaper than existing technologies= premised on leveraging data to help users see what daily elements are aff=cting their blood sugar, and predicting dangerous highs and lows before th=y happen so they can take actions beforehand. To date, their beta app has lowered bl=od sugar averages in over 55% of users 2x better than the world's most=prescribed diabetes drug, Metformin. Which means that with the help of the=date diabetes would need to use less drugs. However I'd agree on complexity of r=gulations and saturation on the market.

<=iv>l run Day One Ventures fund and based in San Francisco. I'd be happ= to connect in person sometime to learn about about your experience and te=l about our investments and opportunities I see.

On Nov 2, 2017, at 3:24 PM, Steven Sinofsk wrote:

Greet=ngs Masha,

I apologize for the delay. I was=at some events in in UK where I did not have connectivity.

## <=div>

I have worked on a related portfolio company so I will have to be=a bit abstract on the specifics of this opportunity. So please excus= these brief thoughts. Also, the only information I had was what was provi=ed in your mail which was limited relative to any detailed analysis.

=div>

* In general the challenge with glucose measurement is =he finger prick. Any device that relies on this will only be marginally be=ter than any other device, whether or not there is software or a slightly =ore convenient measuring device. This is a statement about the inconvenien=e of a prick (and long term challenges) but also the medical challenges on=relying on that point in time.

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

* This is a very crowde= space. There are a lot of apps, a lot of measuring devices, a lot of mixt=res of app and measuring devices. It is very difficult to avoid appearing =s a commodity to consumers.

* While I understand there is potent=al to see innovation using novel approaches to analysis of data, it is not=clear to me how much better the approach can be for an individual with dat=.

## <=div> (cont.)

* The real opportunity I might see is around measuring glucose=or some related telemetry to assist in compliance that is outside the scop= of a finger prick and measuring glucose directly. It seems like we should=have some other data point upon which to apply machine learning.

I hope this helps...any friend of JE is a friend of mine.</=iv>

On Nov 1, 2017, at 4:43 PM, Masha Drokova =rote:

Thank yo= Jeffrey. Steve, great meeting you. Would appreciate your =eedback

On Nov 1, 2017, at 9:31AM, jeffrey E. <jeevacation@gmail.c=m> wrote:

steve ca= you give some guidance

<u>On Wed, Nov 1, 2017 at 12:20</u> PM, Masha Drokova «.

='m looking to invest at the company doing AI-based glucose monitoring =ystem for people with diabetes. We made tech evaluation, talked to a=few experts and more a question about the market and regulations. Likely n=, because it's complex area, but still thinking of them. q=pan>

## </=pan>

May be=someone in your network who knows this area and can advise on this

kind of=tech?

Center Health

Deck:

</ont>https://drive.google.com/file/d/0B5O93D3IJArEbXB=dlI4elVob0U/view
<https://drive.google.com/file/d/0B5O93D3IJArEbXBsdIl4=IVob0U>

<ont face="Roboto-Regular">Our memo: https://docs.google.com/document/d/1M7ZV7VEl8CTnb4EtYWFH2sq5dcC=0PM6g1AlcwrlOtM <https://docs.=oogle.com/document/d/1M7ZV7VEl8CTnb4EtYWFH2sq5dcCm0PM6g1AlcwrlOtM>

Center Health is bui=ding an Al-based glucose monitoring system for the 1 in 11 Americans =ho suffer from diabetes, based on machine learning and their personalized =1, Aria. Users subscribe to their disposable test strips, a 5148/yr U= market, which are delivered monthly, as Aria learns about their diabetes =nd prompts behavioral changes to lower blood sugar. The system i= an order of magnitude cheaper than existing technologies, premised on lev=raging data to help users see what daily elements are affecting their=blood sugar, and predicting dangerous highs and lows before they happen.=C2*

Pros

## </=pan> (cont.)

— 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 iHealth 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
=94 Hardware startup without a product to sell yet, finalizing the developm=nt
— Young team
</=iv>

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