PERSONALIZATION,

CONVERSATIONAL SEARCH

AND

ACTIVE DATA

Confidential © Copyright 2013 256gl, Inc.

Web Evolution – New Challenges and Opportunities

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Hybrid Data = Web Pages + Databases Adaptive Web

Adaptive Web – few notes from the web

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Modeling the user. Where? In client or in server or in client and in server

At the Beginning the Web was the same for everybody

User profile: user knowledge, interest, goals, background, traits

Adaptive client and adaptive server - the problem is how to link two adaptive systems together

What personalization mean in terms of measurable and deliverable solution

1. Search for information2. Server delivers personal content

Personal - providing users what they need without them asking EXPLICITLY. In web - the delivery of dynamic content (textual elements, links, advertisement, product recommendation that are tailored to NEEDS or INTERESTS of particular user

Customization and personalization

Web is not like TV.

The Telephone is the best metaphor

for the Web.

E-Commerce Market Today

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Information Systems Interactive Systems Automated Systems

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The Most Common Component of Today’s Web?

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Honeywell

CampbellBest Buy

UHC

Acura

Insperity

Some of pages are different

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Ikea

Google

Creative Virtual

Nearest Future

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Conversational Search

Willtransform

TraditionalSearch

Voice and Touch Controls

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SIRI: speak to control

Local and remote touch control

Dialog:

Buyer: I’m looking for camera.

SP: Here are a few to choose from. Which do

you like the best ?

Buyer: I think this one. Do you have similar, but

cheaper?

SP: Yes, but these have slightly less features.

Buyer: I like Sony.

SP: Very well. Then look at this one.

Buyer: Any model with better resolution?

SP: This one - Sony MVC. Has Disk Recording

feature.

Buyer: Nice. Just a little too heavy.

etc..

Model Make Price ZOOM SCU

LS443 Kodak $429.99 3 41778439289

EasyShare DX4330 Kodak $296.99 3 41771580506

DiMAGE S304 Minolta $573.49 4 43325992247

DiMAGE 7Hi Minolta $1,196.99 7 43325993374

DiMAGE X Minolta $389.49 3 43325992780

Coolpix 4500 Nikon $631.99 4 18208255030

Coolpix 5700 Nikon $1,047.99 8 18208255047

Coolpix 885 Nikon $469.49 3 18208255054

MVC-CD400 Sony $890.99 3 27242606487

MVC-CD250 Sony $595.99 3 27242606524

MVCCD300 Sony $858.99 3 27242589223

MVC-CD200 Sony $578.99 3 27242589247

Cyber Shot DSC-S75 Sony $494.99 3 27242589278

Cyber Shot DSC-P2 Sony $390.99 3 27242607354

SELECT * FROM Cameras WHERE Price < $800 AND Price > $300

SELECT * FROM Cameras WHERE Price < $800 AND Price > $300

AND MAKE = 'Sony'

SELECT * FROM Cameras WHERE Price < $500 AND Price > $200

AND MAKE = 'Sony' AND ZOOM > 3…

Search as Dynamic Parameterization of the SQL Query

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Data Base:

SQL Interface:

Search as Iterative Process

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Result

Comparative

Analysis

Product’s Characteristics

Feedback

Initial Set of Desired

Characteristics

Error Correction and Learning

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tell me how to get new checks open a new account get my available credit on my credit how do i account balance close account increase the limit on my credit card increase credit limit i would like to know a list of my recent checks

...

Active Data vs. Von Neumann’s Architecture

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Criteria for “Good” Web Site

Kolmogorov: if there is an information, we can obtain it with

minimum number of questions

SELECT X1, X2, … Xk WHERE p1=A1, p2=A2, … pn = An

The length of the answer should be proportional to the length of the question:

L(a) ~ L(q)

L(a) - the length of answer A

L(q) - the length of question Q

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Optimal, converging

process (a)

Converging process with a

surplus information (b)Process does not converge (c)

TimeTimeTime

Info

rma

tio

n

Info

rma

tio

n

Receivedinformation

Receivedinformation

Receivedinformation

Expectedinformation

Info

rma

tio

n

Expectedinformation

Expectedinformation

Linguistic:

Fundamental Problems in Interaction

Mathematic:

tell me how to get new checks {<choice orderNewChecks>}

open a new account {<choice openAccount>}

get my available credit on my credit {<choice getAvailableCredit>}

how do i account balance {<choice getAccountBalance>}

close account {<choice closeAccount>}

increase the limit on my credit card {<choice increaseCreditLimit>}

increase credit limit {<choice increaseCreditLimit>}

i would like to know a list of my recent checks {<choice getRecentChecks>}

loan application {<choice applyLoan>}

i want my available credit on my card {<choice getAvailableCredit>}

apply for a loan {<choice applyLoan>}

i want my balance {<choice getCreditBalance>}

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Questions, statements, and answers areformed in Natural Language, interpretation oftheir meanings depends on the dynamiccontent.

Real-time classification including Fuzzy Logicin High Dimensional Parametric Space.

Fuzzy Linguistic and Contextual Queries

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Neural-Linguistic model for SQL selections:

Neural-mathematical model for object definition in High

Dimensional Parametric Space:

Active Data Implementation

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Active Data Installation

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Personal Wiki, Ideas and 256notes

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Cards

Web Pages

Ideas

Pictures, Voice

SharePoint

FaceBook

Clusters

3D

SavingClassification

Viewing

Structure

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256gl Components

Visualization and

Navigation

Modeling and

Measurement

Sharing and

Collaboration

Interaction and

Personalization

Different Types of Architectures

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Incremental layout algorithm.

EXPLORE_AND_LAYOUT (G , exploreVertex )1 G' = (V ', E' ) ← ExploreVertex( exploreVertex )2 T ← ∅3 for e ach edge e ∈ (E'−E) do4 s ← sourceVertex (e)5 d ← destinationVertex (e)6 if s = exploreVertex then7 v ← d8 else9 v ← s10 if v ∉V then11 V ← V ∪{v}12 T ← T ∪ {v}13 // v is not an explored vertex since e ∈ (E'−E)14 E ← E ∪{e}15 DO_LAYOUT (T , exploreVertex )DO_LAYOUT (T , v)max_w ← MaxLabelWidth (T )angle ← 2 ∗π / sizeof (T )counter = 0for e ach vertex u ∈ T doX ← sin(angle*counter) * max_wY ← cos(angle*counter)*max_wu[x] ← v[x] + Xu[ y] ← v[ y] + Ycounter← counter+1

Algorithmic Architecture

Scenarios Architecture

Software Architecture Abstract Architecture

Ontology is a triple O = (C, S, isa) where:

C = {c1, c2, . . . , cm} is a set of classes, where each class ci refers to a set of real world objects (class instances).

S ={s1, s2, . . . , sn} is a set of slots, where each slot si could refer to:a property of a class: a value of a simple type such as Integer, String or Date;a binary typed role: the representation of a relation between classes.

isa ={isa1, isa2, . . . , isap} is a set of inheritance relationships defined between classes. Inheritance relationships carry subset semantics and define a partial order over classes, organizing classes into one or more tree structures.

In order to accommodate the individual instances, this definition can be extended with a fourth element I = {i1, i2, . . . , iq}, where each iw is an instance of some class cx ∈ C. The instance includes a concrete value for every slot sy associated with cx or its ancestors (as defined by the isa set).

256GL – Tools for Adaptive Web Designers

Virtual Machines

Data Bases

Programming Languages

Developers’ Environment

• Biocomputers

• Memristors

• Multi-core

• Visual Studio

• Cold Fusion

• Oracle developer

• JavaScript

• XML

• Java VM

• SQL

• Object DB

• XML DB

• Semantic DB

Neural Language(NQL, APIs)

Neural Network Oriented Data

Neural Virtual Machine(Interpreters and APIs)

Neural Environment(QBF/QBE, Editors, IDE)

FIND THE CAR WHICH COLOR=RED;

PRICE<15,000; MAKER=FORD…

SDK

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• Stems from research in linguistic, programming, mathematic, bio-

Informatics, and marketing

• Applied solutions are Rich-Clients prototypes of the future scalable and

transferable HTML-5 based Web-applications

• Time proven concepts like HyperCard has been reworked for the new

technologies and Web capabilities

• Natural development of the system based on concept of Agile

• Both uniqueness and keystone to success for 256gl reside in the art of

“connectionism”

256gl – Synergy of Business, Technology and Art

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256gl is the result of decades-longresearch, experiments, anddevelopment in the field ofInteractive Systems

Components

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256DZ 256Editor 256Notes

256 Server 256notes

Presentation in high resolution using - DeepZoom, dialog support, individual neural datastructure, client/server interfaces.

Dialogs and Reactions editor. Tutoring andupdating of Neural components, design ofasynchronous and linear question-answersequences.

Facts and context editor. Preparation offactual information, data clusters andcontext groups forming. Publishing andediting of visual as well as textualinformation.

Personal profile. Presentation Management. Voice Management.

256 Service

IIS or Tomcat Web server for all 256gl components, including asynchronous protocols, DB interfaces, etc.

Copying and synchronization software utilities for external Data Bases, like BestBuy, CMDData, and others.

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History

1976 201019881980 1984 1991 1994 1999 2004 2006

CODIAL (Coding for Dialogs)

Software for developing

asynchronous dialogues based

on Lisp, string manipulation and

parallel processing.

Dialogue and AI based

Executives Decision Making

developed.

Theory of quasi –parallelism and

multiple-meaning variables for

asynchronous dialogues, spatial

modeling, R-Functions.

HyperCard XML extensions for dynamic

flowcharts and supporting

Question/Answer flow developed.

Reverse Decision Making System,

based on Neural Network

developed for Medical Diagnostics.

Personal Servers and Objects Sharing

(HyperHTTP / HyperTinker). Neural

Core and knowledge processor

created.

Neural Reader for Web, capable to

learn, read, parse and classify

pages from the Internet.

Virtual Agents based on Neural

Core developed for Auto Dealers

Web sites.

Theory of neural programming for

interactive Web published.

Embedded Neural Engine used to

build personal search and

knowledge organizer.

Igor Perechod

Yuri Shevlyakov

Elena Illovayskaya

EkaterinaYuschenko

Anatoly Smolyar

Kirill Toskin

Oleg Scherbina

Vadim Ivanov

Oded Susskind

Alan Hofer

Yuri Shestov

Robert Freidson

Donald KnutLee Felsenstein

Ted Nelson

Jeff O’Dell

Vladimir PavlovJim Kephard

Judson Bemis

Larissa Felonyuk

Oleg Pasynkov

David Kunin

Victor Yekelchik

Vladimir Cherkassky

Theory and the model of self

replicated software created.

Value

Random Key generator for

CODASYL – Network Database

IDS/COBOL was developed.

Sergey Tolkachev Artem Zaborsky

Boaz Vinogradov

Mike O’Dell

Sergey Garbuzov

Anatoly Arsenyev

Nurorlinks between multiple

layers developed.

Thomas McEnery

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Participants

Business Development

Georges Selvais

Inventor

Lee Felsenstein

Design

Oleg Pasynkov

Project Leader

Jim Kephart

Software Developer

Vadim Ivanov

Mathematics

Dr. Vladimir Cherkassky

Software Developer

Artem Zaborsky

Project Manager

Larissa Felonyuk

Marketing & Sales

Victor Yekelchik

System Architect

Dr. Sergey Tolkachev

Mathematics

Dr. Robert Freidson

Adviser

Judson Bemis

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Business Development

Boaz Vinogradov

Dealers

Call CentersPersonal Search

and Knowledge

Organization

RetailServices

Investments

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Markets

Medical

Diagnostics

Technology

Solutions

Individuals

Web Developers

Corporations

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Portfolio

Beautopia, Inc.Beauty and Hair aid consultant

Sunnyside Acura

Interactive car search

1000+1 Doors

Interactive Search for Doors retailer

Nemo Ltd. for TGK-1Context Search and Reference System for Accounting

CITCKnowledge Management System - Personal Wiki

256gl – License Certificate

Sales Plan

Market

Segment

Current Market

Annual Generic

Software License

Pricing

256gl

License

Price

Beta Year 1 Year 2 Year 3 Year 4 Year5Market

Size

Corporations $200,000 + $225,000 0 1 2 5 8 30 3,000 +

Large

Business$125,000 - $225,000 $125,000 0 5 20 75 500 1500 50,000 +

Mid-size

Business$20,000 - $125,000 $45,000 2 3 10 50 200 500 150,000 +

Small

Business$200 - $50,000 $500 2 50 200 2,000 20,000 200,000 1,000,000 +

Open Source Free Free 1000 5,000 10,000 25,000 100,000 250,00010,000,000

+

Total $1М $3,5М $14М $80М $300М

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Looking for partners capable of investing $4-$6 million in 256gl, Inc.

Use of the funding:

• From Alpha and Beta to commercial releases

• Technological infrastructure in the USA, R&D in Saint Petersburg, Russia, TNU Crimea, Ukraine

• Sales and marketing

• Business structure to prepare 256gl, Inc. for 3rd round of investments to facilitate IPO or sale of the company

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What we need

256GL, Inc. current stock - 10 million shares

Convert to $10M – $100M in 3 years

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Financial Goal and Exit Strategy

How Big is the Idea?

Neural Structures are prototypes of the future intelligent data models.They may become a foundation for the new generation of DBMS as wellas the key element of VAR, where Value comes from knowledge addedby developers as much as users, allowing VARs’ active participation inthe sales process.

Reverse Advertisement - personal profiles, parametrical definition of products allowing advertisement industry to turn from buyers to manufacturers, facilitating individual oriented and flexible manufacturing in the near future. Reverse Advertisement represents new services market, where large sums of money will be spent in the nearest future.

Speakable Web (SPA) - the new Web that supports interaction usingvoice. Informational kiosks, personal computers and telephonesintegration, voice operation on TVs and other devices - all of thoserepresent new markets that are being born now.

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Sergey TolkachevFounder, CEO

Office: 952-942-5123Mobile: 612-670-0585

Web: http://256gl.comhttp://nnod.com

Email: stolkachev@256gl.com

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