Product Mix & Match: Leverage Computer Vision for Data Input

Product mix&match
Technology high-level overview
By Royi Benyossef (+royiby)

Product mix&match by Royi Benyossef of Samsung’s innovation center in Tel-Aviv (royiby@gmail.com)
Android developer since 2009
Royi Benyossef

GDG Herzliya co-founder
Royi Benyossef

Android speaker and lecturer
Royi Benyossef

Android Mentor at * accelerator
Royi Benyossef

Android GDE 2013, 2014, 2015
Royi Benyossef

Android GDE 2013, 2014, 2015
Developer relations manager Samsung GIC
Royi Benyossef

American fund
Samsung innovation center in Tel-Aviv

American fund
Interested in SW and services startups (pre-seed to series B)

American fund
HQ in Mountain View California (not Korea)

American fund
Offices in SF, NY and now Tel-Aviv

American fund
Starting with early stage (up to $500K)

American fund
Community programs are Free-for-all, no strings attached

American fund
Community programs are Free-for-all, no strings attached
Contact me: royiby@gmail.com

Motivation
(What do we want?!)

Motivation
Product Answers a need

Motivation
(to someone other than you)

Motivation
(to someone other than you)
(hopefuly to many someones)

Motivation
Better than the competition

Motivation
Better than the competition:
- First!

Motivation
First!
- Does more

Motivation
First!
- Does more (valuable stuff please)

Motivation
First!
Does more
- Easier to use

HOW?

Motivation
- Product
Technology
Reduce “redundant effort” friction

Motivation
- Product
Technology
(= Help users “work” less)

Motivation
- Product
Technology
Be a ginny

Motivation
- Product
Technology
Be a ginny
(= Anticipate the user’s wishes)

Motivation
- Product
- Technology
Bonus
Create an “unfair advantage”

Motivation
- Product
- Technology
Bonus
Create an “unfair advantage”
(= Make it harder to copy you)

Use all the
technologies?!

Usecase
(Learning by example)

Usecase
Premise UberPooch is the company

Usecase
Premise UberPooch:
- For service dogs (their owners)

Usecase
Premise UberPooch:
For service dogs (their owners)
- Offers:
> On-site grooming

Usecase
Premise UberPooch:
- Offers:
On-site grooming
> Care aid (vets, dog walking)

Usecase
Premise UberPooch:
- Offers:
On-site grooming
Care aid (vets, dog walking)
> Play-date arrangement

Not sure ifstupid...
or genius!

Usecase
- Premise
Challenges
Data input

Usecase
- Premise
Challenges
Data input
Immersive experience

Usecase
- Premise
Challenges
Data input
Business logic optimizations

Usecase
- Premise
Challenges
Data input
Business logic optimizations
Scale

With a little help from my libs by Royi Benyossef (+royiby)

Input
(All the data, hold the friction)

Input
Problem You need data to run the service

Input
Manual data insertion is not fun

Input
Social data extraction is not enough

Input
Social data extraction is not enough
(= Data from Facebook/G+ login)

Input
- Problem
Solutions:
> CV
CV = Computer vision

Input
- Problem
Solutions:
> CV
Image recognition & understanding

Input
- Problem
Solutions:
> CV
(= Item count & identification)

Input
- Problem
Solutions:
> CV
Neural networks

Input
- Problem
Solutions:
> CV
Neural networks
(= IR/IU + classification, relationships)

Input
- Problem
Solutions:
> CV
Neural networks
Temporal neural networks

Input
- Problem
Solutions:
> CV
Neural networks
(= NN + time = inter-item interactions)

Input
- Problem
Solutions:
> CV
Neural networks
Optical character recognition (OCR)

Input
- Problem
Solutions:
> CV
Neural networks
Optical character recognition (OCR)
(= Text extraction from images)

Input
- Problem
Solutions:
CV
> NLP
NLP = Natural language processing

Input
- Problem
Solutions:
CV
> NLP
(= Extracting fields from free text)

Input
- Problem
Solutions:
CV
> NLP
(= Extracting fields from free text)
(= Vocal w/ TTS or from external text)

Immersiveness
(Delight your users in new and exciting ways)

Immersiveness
Problem Web & mobile might not be enough

Immersiveness
- Problem
Solutions:
> UX
= User’s perception experience

Immersiveness
- Problem
Solutions:
> UX
AR Vs. VR:
> AR = Augmented reality

Immersiveness
- Problem
Solutions:
> UX
AR Vs. VR:
> AR = Augmented reality
(= Virtual layered over the real)

Immersiveness
- Problem
Solutions:
> UX
AR Vs. VR:
AR = Augmented reality
> VR (Virtual reality)

Immersiveness
- Problem
Solutions:
> UX
AR Vs. VR:
AR = Augmented reality
> VR (Virtual reality)
(= Virtual replaces the real)

Immersiveness
- Problem
Solutions:
> UX
AR/VR
360o
Vs. 3D:
> 3D = Depth capturing

Immersiveness
- Problem
Solutions:
> UX
AR/VR
360o
Vs. 3D:
3D = Depth capturing
> 360o
= Sphere capturing

Immersiveness
- Problem
Solutions:
> UX
AR/VR
360o
Vs. 3D:
3D = Depth capturing
360o
= Sphere capturing
> Both!

Immersiveness
- Problem
Solutions:
UX
> Devices
= Seamlessness and ubiquitousness

Immersiveness
- Problem
Solutions:
UX
> Devices
IOT/IOE

Immersiveness
- Problem
Solutions:
UX
> Devices
IOT/IOE (= internet of things/everything)

Immersiveness
- Problem
Solutions:
UX
> Devices
IOT/IOE (= internet of things/everything):
> Wearables

Immersiveness
- Problem
Solutions:
UX
> Devices
> Wearables
(= watches, glasses, ear pieces etc.)

Immersiveness
- Problem
Solutions:
UX
> Devices
Wearables
> Smart venues

Immersiveness
- Problem
Solutions:
UX
> Devices
Wearables
> Smart venues (home, cities, etc.)

Immersiveness
- Problem
Solutions:
UX
> Devices
Wearables
> Smart venues (home, cities, etc.)
(= Monitoring & automation)

Immersiveness
- Problem
Solutions:
UX
> Devices
Wearables
Smart venues
> Autonomousness

Immersiveness
- Problem
Solutions:
UX
> Devices
Wearables
Smart venues
> Autonomousness (cars, drones, etc.)

Immersiveness
- Problem
Solutions:
UX
> Devices
Wearables
Smart venues
> Autonomousness (cars, drones, etc.)
(= reducing dependence in people)

Immersiveness
- Problem
Solutions:
UX
Devices
Notes
In AR/VR content creation:
> Post-prod time is important

Immersiveness
- Problem
Solutions:
UX
Devices
Notes
Post-prod time is important
> Creation equipment is costly

Immersiveness
- Problem
Solutions:
UX
Devices
Notes
Post-prod time is important
Creation equipment is costly
> Requires viewers

Logic optimizations
(Automate and reduce costs of time, HR and money)

Logic optimizations
Problem You want to improve your service

Logic optimizations
Problem You want to improve your service’s:
> Efficiency

Logic optimizations
Efficiency
> Response time to users

Logic optimizations
Efficiency
Response time to users
> Effectiveness and accuracy

Logic optimizations
You (may) have data

Logic optimizations
You (may) have data
> About your users

Logic optimizations
You (may) have data
About your users
> About users in general

Logic optimizations
- Problem
Solutions:
Big data
Using the cloud to analyze data

Logic optimizations
- Problem
Solution:
Big data
Deep Vs. Machine

Logic optimizations
- Problem
Solution:
Big data
Deep Vs. Machine:
> Machine learning

Logic optimizations
- Problem
Solution:
Big data
Deep Vs. Machine:
> Machine learning
(= Pre-fed with existing data sets)

Logic optimizations
- Problem
Solution:
Big data
Deep Vs. Machine:
Machine learning
> Deep learning

Logic optimizations
- Problem
Solution:
Big data
Deep Vs. Machine:
Machine learning
> Deep learning
(= Fed with live usage data)

Logic optimizations
- Problem
Solution:
Big data
Notes
Deep learning requires critical mass

Logic optimizations
- Problem
Solution:
Big data
Notes
Pre-existing data sets are hard to get

Logic optimizations
- Problem
Solution:
Big data
Notes
Pre-existing data sets are hard to get
Big data is cloud costly!

Scale
(Reaching it quickly, keeping it w/o downtime)

Scale
Problem You want to reach the market quickly

Scale
You want to “go viral”

Scale
You want to “save on” a cloud team

Scale
You want to “save on” a cloud team
(= or you don’t have one)

Scale
- Problem
Solutions:
> IaaS
= Infrastructure as a service

Scale
- Problem
Solutions:
> IaaS
Same as getting a local server

Scale
- Problem
Solutions:
> IaaS
Deploy more remotely & instantly

Scale
- Problem
Solutions:
> IaaS
Deploy more remotely & instantly
Example = AWS EC2

Scale
- Problem
Solutions:
IaaS
> PaaS
= Platform as a service

Scale
- Problem
Solutions:
IaaS
> PaaS
Allows you to run your backend code

Scale
- Problem
Solutions:
IaaS
> PaaS
Offers you “corner cutters”

Scale
- Problem
Solutions:
IaaS
> PaaS
Offers you shortcuts
(Like CDN, caching, stabilizers etc.)

Scale
- Problem
Solutions:
IaaS
> PaaS
Offers you shortcuts
Example = Google app engine (GAE)

Scale
- Problem
Solutions:
IaaS
PaaS
> BaaS
= Backend as a service

Scale
- Problem
Solutions:
IaaS
PaaS
> BaaS
(= form of Software as a service or SaaS)

Scale
- Problem
Solutions:
IaaS
PaaS
> BaaS
Highly managed backend

Scale
- Problem
Solutions:
IaaS
PaaS
> BaaS
(= limited to no code, generic impl.)

Scale
- Problem
Solutions:
IaaS
PaaS
> BaaS
Examples = Back&, RapidAPI, Firebase

Scale
- Problem
Solutions:
IaaS
PaaS
BaaS
Notes
Migration difficulties

Scale
- Problem
Solutions:
IaaS
PaaS
BaaS
Notes
High scale payments

Scale
- Problem
Solutions:
IaaS
PaaS
BaaS
Notes
High scale payments
Traction fluctuation management

Wrap-up
(Circling back to our example)

Wrap-up
Premise UberPooch:
- Offers:
On-site grooming
Care aid (vets, dog walking)
Play-date arrangement

Wrap-up
- Premise
Input
Social extraction (basic)

Wrap-up
- Premise
Input
Textual and STT NLP

Wrap-up
- Premise
Input
Textual and STT NLP
- Typing is hard!

Wrap-up
- Premise
Input
Textual and STT NLP
Typing is hard!
- The disabled might have is harder

Wrap-up
- Premise
Input
Textual and STT NLP
Temporal neural network

Wrap-up
- Premise
Input
Textual and STT NLP
Temporal neural network:
- Emergency detection (with IOT)

Wrap-up
- Premise
- Input
Immersion
Wearables

Wrap-up
- Premise
- Input
Immersion
Wearables
- STT aids

Wrap-up
- Premise
- Input
Immersion
Wearables
STT aids
- Geolocation

Wrap-up
- Premise
- Input
Immersion
Wearables
IOT (smart home)

Wrap-up
- Premise
- Input
Immersion
Wearables
IOT (smart home)
- Emergency information

Wrap-up
- Premise
- Input
Immersion
Wearables
IOT (smart home)
Emergency information
- Emergency assistance

Wrap-up
- Premise
- Input
- Immersion
Logic
Deep learning to improve offering

Wrap-up
- Premise
- Input
- Immersion
Logic
Deep learning to improve offering:
> Effective connections

Wrap-up
- Premise
- Input
- Immersion
Logic
Deep learning to improve offering:
Effective connections
> More efficient routes

Wrap-up
- Premise
- Input
- Immersion
- Logic
Scale
BaaS for POC and MVP

Wrap-up
- Premise
- Input
- Immersion
- Logic
Scale
BaaS for POC and MVP
If we get there… the rest is good!

With a little help from my libs by Royi Benyossef (+royiby)
Summary
(What did we have here?)

Deconstruct your product
Summery

- Deconstruct your product
Create an unfair advantage with technology
Summery

- Deconstruct your product
- Create an unfair advantage with technology
Never reinvent the wheel
Summery

“We should be building great
things. Things that Don’t yet
exist”

I hope you liked it
Thank you for listening
By Royi Benyossef (+royiby)

Product Mix & Match: Leverage Computer Vision for Data Input

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