Product mix&match - a technology HLD overview with a case-study example

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 ([email protected])

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: [email protected]


Prolog

The what & why of this


Motivation

(What do we want?!)


Motivation

Product Answers a need


Motivation

Product Answers a need(to someone other than you)


Motivation

Product Answers a need(to someone other than you)(hopefuly to many someones)


Motivation

Product Answers a needBetter than the competition


Motivation

Product Answers a needBetter than the competition:

- First!


Motivation


First!- Does more


Motivation


First!- Does more (valuable stuff please)


Motivation


First!Does more- Easier to use


Motivation


First!Does more- Easier to use


HO

W

?


Motivation

- Product

Technology Reduce “redundant effort” friction


Motivation

- Product

Technology Reduce “redundant effort” friction(= Help users “work” less)


Motivation

- Product

Technology Reduce “redundant effort” frictionBe a ginny


Motivation

- Product

Technology Reduce “redundant effort” frictionBe a ginny

(= Anticipate the user’s wishes)


Motivation

- Product- Technology

BonusCreate an “unfair advantage”


Motivation

- Product- Technology

BonusCreate an “unfair advantage”

(= Make it harder to copy you)


Use a

ll t

he

tech

no

log

ies?!


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


On-site grooming> Care aid (vets, dog walking)


Usecase


On-site groomingCare aid (vets, dog walking)> Play-date arrangement

Not sure if stupid...

or genius!


Usecase

- Premise

Challenges Data input


Usecase

- Premise

Challenges Data inputImmersive experience


Usecase

- Premise

Challenges Data inputImmersive experienceBusiness logic optimizations


Usecase

- Premise

Challenges Data inputImmersive experienceBusiness logic optimizationsScale

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

Options

What’s out there?!


Input

(All the data, hold the friction)


Input

Problem You need data to run the service


Input

Problem You need data to run the serviceManual data insertion is not fun


Input

Problem You need data to run the serviceManual data insertion is not funSocial data extraction is not enough


Input

Problem You need data to run the serviceManual data insertion is not funSocial data extraction is not enough

(= Data from Facebook/G+ login)


Input

- Problem

Solutions:> CV

CV = Computer vision


Input

- Problem

Solutions:> CV

CV = Computer visionImage recognition & understanding


Input

- Problem

Solutions:> CV

CV = Computer visionImage recognition & understanding

(= Item count & identification)


Input

- Problem

Solutions:> CV

CV = Computer visionImage recognition & understandingNeural networks


Input

- Problem

Solutions:> CV

CV = Computer visionImage recognition & understandingNeural networks

(= IR/IU + classification, relationships)


Input

- Problem

Solutions:> CV

CV = Computer visionImage recognition & understandingNeural networksTemporal neural networks


Input

- Problem

Solutions:> CV

CV = Computer visionImage recognition & understandingNeural networksTemporal neural networks

(= NN + time = inter-item interactions)


Input

- Problem

Solutions:> CV

CV = Computer visionImage recognition & understandingNeural networksTemporal neural networks Optical character recognition (OCR)


Input

- Problem

Solutions:> CV

CV = Computer visionImage recognition & understandingNeural networksTemporal neural networks Optical character recognition (OCR)

(= Text extraction from images)


Input

- Problem

Solutions:CV

> NLP

NLP = Natural language processing


Input

- Problem

Solutions:CV

> NLP

NLP = Natural language processing(= Extracting fields from free text)


Input

- Problem

Solutions:CV

> NLP

NLP = Natural language processing(= 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

= User’s perception experienceAR Vs. VR:

> AR = Augmented reality


Immersiveness

- Problem

Solutions:> UX


> AR = Augmented reality(= Virtual layered over the real)


Immersiveness

- Problem

Solutions:> UX


AR = Augmented reality> VR (Virtual reality)


Immersiveness

- Problem

Solutions:> UX


AR = Augmented reality> VR (Virtual reality)

(= Virtual replaces the real)


Immersiveness

- Problem

Solutions:> UX

= User’s perception experienceAR/VR360o Vs. 3D:

> 3D = Depth capturing


Immersiveness

- Problem

Solutions:> UX


3D = Depth capturing > 360o = Sphere capturing


Immersiveness

- Problem

Solutions:> UX


3D = Depth capturing 360o = Sphere capturing> Both!


Immersiveness

- Problem

Solutions:UX

> Devices

= Seamlessness and ubiquitousness


Immersiveness

- Problem

Solutions:UX

> Devices

= Seamlessness and ubiquitousnessIOT/IOE


Immersiveness

- Problem

Solutions:UX

> Devices

= Seamlessness and ubiquitousnessIOT/IOE (= internet of things/everything)


Immersiveness

- Problem

Solutions:UX

> Devices

= Seamlessness and ubiquitousnessIOT/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


WearablesSmart venues> Autonomousness


Immersiveness

- Problem

Solutions:UX

> Devices


WearablesSmart venues> Autonomousness (cars, drones, etc.)


Immersiveness

- Problem

Solutions:UX

> Devices


WearablesSmart venues> Autonomousness (cars, drones, etc.)

(= reducing dependence in people)


Immersiveness

- Problem

Solutions:UXDevices

Notes

In AR/VR content creation:> Post-prod time is important


Immersiveness

- Problem

Solutions:UXDevices

Notes

In AR/VR content creation:Post-prod time is important> Creation equipment is costly


Immersiveness

- Problem

Solutions:UXDevices

Notes

In AR/VR content creation:Post-prod time is importantCreation 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

Problem You want to improve your service’s:Efficiency> Response time to users


Logic optimizations

Problem You want to improve your service’s:EfficiencyResponse time to users> Effectiveness and accuracy


Logic optimizations

Problem You want to improve your serviceYou (may) have data


Logic optimizations


> About your users


Logic optimizations


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

Using the cloud to analyze dataDeep Vs. Machine


Logic optimizations

- Problem

Solution:Big data

Using the cloud to analyze dataDeep Vs. Machine:

> Machine learning


Logic optimizations

- Problem

Solution:Big data


> Machine learning(= Pre-fed with existing data sets)


Logic optimizations

- Problem

Solution:Big data


Machine learning> Deep learning


Logic optimizations

- Problem

Solution:Big data


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

Deep learning requires critical massPre-existing data sets are hard to get


Logic optimizations

- Problem

Solution:Big data

Notes

Deep learning requires critical massPre-existing data sets are hard to getBig data is cloud costly!


Scale

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


Scale

Problem You want to reach the market quickly


Scale

Problem You want to reach the market quicklyYou want to “go viral”


Scale

Problem You want to reach the market quicklyYou want to “go viral”You want to “save on” a cloud team


Scale

Problem You want to reach the market quicklyYou want to “go viral”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

= Infrastructure as a serviceSame as getting a local server


Scale

- Problem

Solutions:> IaaS

= Infrastructure as a serviceSame as getting a local serverDeploy more remotely & instantly


Scale

- Problem

Solutions:> IaaS

= Infrastructure as a serviceSame as getting a local serverDeploy more remotely & instantlyExample = AWS EC2


Scale

- Problem

Solutions:IaaS

> PaaS

= Platform as a service


Scale

- Problem

Solutions:IaaS

> PaaS

= Platform as a serviceAllows you to run your backend code


Scale

- Problem

Solutions:IaaS

> PaaS

= Platform as a serviceAllows you to run your backend codeOffers you “corner cutters”


Scale

- Problem

Solutions:IaaS

> PaaS

= Platform as a serviceAllows you to run your backend codeOffers you shortcuts

(Like CDN, caching, stabilizers etc.)


Scale

- Problem

Solutions:IaaS

> PaaS

= Platform as a serviceAllows you to run your backend codeOffers you shortcutsExample = Google app engine (GAE)


Scale

- Problem

Solutions:IaaSPaaS

> BaaS

= Backend as a service


Scale

- Problem

Solutions:IaaSPaaS

> BaaS

= Backend as a service (= form of Software as a service or SaaS)


Scale

- Problem

Solutions:IaaSPaaS

> BaaS

= Backend as a service Highly managed backend


Scale

- Problem

Solutions:IaaSPaaS

> BaaS

= Backend as a service Highly managed backend

(= limited to no code, generic impl.)


Scale

- Problem

Solutions:IaaSPaaS

> BaaS

= Backend as a service Highly managed backendExamples = Back&, RapidAPI, Firebase


Scale

- Problem

Solutions:IaaSPaaSBaaS

Notes

Migration difficulties


Scale

- Problem


Notes

Migration difficultiesHigh scale payments


Scale

- Problem


Notes

Migration difficultiesHigh scale paymentsTraction fluctuation management


Wrap-up

(Circling back to our example)


Wrap-up


On-site groomingCare aid (vets, dog walking)Play-date arrangement


Wrap-up

- Premise

Input Social extraction (basic)


Wrap-up

- Premise

Input Social extraction (basic)Textual and STT NLP


Wrap-up

- Premise


- Typing is hard!


Wrap-up

- Premise


Typing is hard!- The disabled might have is harder


Wrap-up

- Premise

Input Social extraction (basic)Textual and STT NLPTemporal neural network


Wrap-up

- Premise

Input Social extraction (basic)Textual and STT NLPTemporal neural network:

- Emergency detection (with IOT)


Wrap-up

- Premise- Input

ImmersionWearables


Wrap-up

- Premise- Input

ImmersionWearables

- STT aids


Wrap-up

- Premise- Input

ImmersionWearables

STT aids- Geolocation


Wrap-up

- Premise- Input

ImmersionWearablesIOT (smart home)


Wrap-up

- Premise- Input


- Emergency information


Wrap-up

- Premise- Input


Emergency information- Emergency assistance


Wrap-up

- Premise- Input- Immersion

Logic

Deep learning to improve offering


Wrap-up


Logic

Deep learning to improve offering:> Effective connections


Wrap-up


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 MVPIf 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)

Bye bye!

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