A year salary for knowledge workers is about six trillion dollars.
How this was calculated: According to USA statistics (https://www.bls.gov/news.release/pdf/empsit.pdf
about 160 million people are employed in that country.
About 70% are so called “knowledge workers” dealing with
With the average salary about $45k (https://www.thebalancecareers.com/average-salary-information-for-us-workers-2060808
their year salary is about $6 billion.
Google is the best example in this market space. Google
is a universal engine providing hundreds links in response to every query,
helping everyone in a universal way and according to our estimate saving about
10% time, or about 600 billion dollars a year.
Big companies and startups are
raising billions of dollars to employ AI in every area of business and consumer
Internet Technology University (ITU) focuses on Accelerating Sharing Knowledge with Conversational
Semantic Decision Support (AskCSDS) systems.
Our approach, supported by several patents, combines components of artificial
intelligence with knowledge engineering expertise.
We develop methods and tools (AskCSDS
) for building client-defined knowledge factories and
related applications, which target estimated 90 billion dollar market.
Google is a universal search engine, we focus on specialized knowledge domains,
where Subject Matter Experts (SME) can increase their efficiency by using AskCSDS
Knowledge Delivery as a Service
The main scenario of Knowledge Delivery as a Service
includes several customization steps:
engages a client into a conversation to define and refine daily the area of
The service uses
publicly available sources to collect defined by a client
includes a semantic engine to retrieve meaningful information from many data
The service effectively
creates specialized knowledge domains in the client defined areas.
Greatly improving search, knowledge
domains open more opportunities described below.
From capturing “Tribal Knowledge” to Conversational Design and Moree…
The conversational approach to knowledge acquisition
combines the power of Big Data and Semantic Technologies with the human intuition
to create a Corporate Knowledge Factory as a base for Conversational Decision
For example, the Adaptive Robot System
(US patent) can converse with a SME to
retrieve and translate “tribal knowledge” into rules, scenarios, and services.
This is a starting point for more ambitious use cases.
Integrated software and knowledge engineering leads to truly collaborative (human-robot)
Client Benefits: Use Cases
1. Reducing IT budget while creating a unified landscape for all information systems
Enterprise IT as
we know it today is slowly disappearing. Companies have begun transitioning
their IT to a cloud. But we offer even a bigger transformation, which makes
transitioning to a cloud much more efficient. Yes, Enterprise IT can transformed
from current enormous complexity and become very simple.
More than 50% of
IT budget is currently dedicated to managing technical systems, not managing
information. Why? – Historically, different types of information has been
processed by different systems.
Semantic Technologies change this by
offering a unified landscape for all types of information.
Wait a minute! Specific
data tables in specific
applications make specific
perform faster. True! But in the increasingly interconnected business,
integration efforts outweigh the benefits of specific approaches to specific
data. And the growing art and science of Big Data helps us understand a complete business story
instead of having to piece it
Smart cloud services collect enterprise
information in a unified knowledge component, corporate knowledge factory,
improving opportunity for automation, while greatly reducing cost of IT.
2. Increasing business efficiency and providing decisive advantage over similar businesses
by integrating structured and unstructured data and collecting
“tribal knowledge” in the corporate knowledge factory.
Every company is striving to become a leading business in its business area.
Companies invest in smarter people and smarter technology.
Smart cloud services offer an optimal combination of both.
Conversational Semantic Decision System (CSDS), described
in the patents Knowledge-Driven Architecture
Adaptive Robot Systems
helps transforming multiple forms of information into a knowledge domain.
Multiple CSDS can collaborate with each other to connect knowledge branches.
CSDS can also converse with a SME to retrieve and translate “tribal knowledge”
into rules, scenarios, and services.
Conversational approach to knowledge acquisition combines the power of Big Data and Semantic
Technologies with the human intuition. This combination effectively creates a
Corporate Knowledge Factory as a base for Conversational Semantic Decision Support (CSDS) systems.
Integrated software and knowledge engineering leads to truly collaborative (human-computer) development.
Development life cycle with its multiple teams of business, development and maintenance teams will be replaced by Conversational Development and Manufacturing (CDM).
A powerful combination of human intuition and computer restlessness is expanded by the conversational approach. CDM uses ontology to map human instructions to the knowledge tree. CDM has access to corporate knowledge factory and also connected to related knowledge domains. These connections allows CDM to fill in “know how” - technical details, which may include corporate policies and regulations as well as business process instructions and usually consume most of development time.
An Adaptive Robot system
is a sample of such a Development Factory.
Adaptive robot systems (US Patent 7966093)
can learn on-the-go and build new skills, while providing on-the-fly translations of situational requirements into adaptive behavior models and further down to service scenarios for a collaborative robot team.
The use case expands on Service-Oriented Architecture. Orchestrated services are assembled into business scenarios and applications.
The invention integrated SOA with Knowledge Engineering to allow resolving new situations via computer-human collaboration. Built-in the system knowledge domain (ontology) helps a computer be a bit smarter by asking questions to refine instructions.
This invention is improving robot-to-robot and robot-to-human conversational interface and providing on-the-fly translations of situational requirements into adaptive behavior models and further down to service scenarios for a collaborative robot teams, effectively building new robotic/computer skills.
An example of such distributed collaborative work of robots and SMEs in conversational mode is provided below with a use case related to the military field.
On the image below, a subject matter expert sends the order "Clear Mine Field" to a robot system.
One or more robots, which is specialized in the Military operations, will intercept the order and subscribe as potential participants to this request. This will start a conversation between the system and the sender of the order. This conversation will result in a formatted scenario to be executed as a set of orchestrated services. The Conversation Manager will interact with the Scenario Formatter and check with the Service Dictionary to see if a scenario has been completed and can be executed.
Client benefits will drive our company profit.
Clients will be able to cut their IT expenses
and increase business productivity by semi-automation in decision-making
processes. Estimated client benefit of using smart clouds is about 10% of their
budget for knowledge workers (10% of $60 bln is $6 bln). Our conservative
estimate is that clients will pass about 5% of their benefit to pay for our
smart cloud products (5% of $6bln is $300mln)
We expect about 50% of our company revenue from Product Licenses
Another 30% of revenue is expected from customization and support provided by our consulting team. We plan to initiate this work free for high visibility
as our investment to gain name recognition.
Then this channel will generate constantly increasing revenue.
Allow common access to knowledge domains excluding client-specific knowledge branches
This will open an opportunity for
advertising and add additional 10% to revenue.
3. Creating a new education paradigm will bring an additional 10% revenue
Current Formula of Education:
- Colleges and Universities serve as the main channel to access education<
have hard time finding work
Internet Technology University (ITU) and Semantic Technology is changing the formula of
A Conversational Semantic Decision Support (see references and patents below) not only helps students by
optimizing Individual Learning Process.
CSDS also helps SMEs to transfer their knowledge into educational
A new Educational Platform by ITU effectively Expands Education beyond Academy by helping SME, who wants to share, becoming a SME-instructor, teaching skills
that are needed today and tomorrow, directly connecting students with Job
This will reduce the necessity for brokerage between a student and a profession.
This is done in other industries. Smart applications such as Uber remove the necessity for brokers - receptionists at taxi stations. Smart applications directly connect consumers and producers.
Professional education will become less dependent on brokers, such as Academia and job agencies. Smart applications with CSDS will streamline professional education, directly connecting students and jobs.
will finally be in a position to grow Global Knowledge Marketplace and to offer templates (conversational scripts) helping authors, first of all the SMEs, to share their unique knowledge.
, which often have the best SMEs in a specific knowledge domain, will become invaluable knowledge resources. The system/platform helps SMEs sharing their unique knowledge in multiple ways, including Teaching-by-samples, Test-driven-study, and more. Some of these ways, such as Test-driven-study can be used for screening potential candidates.
companies, such as IBM, Google, Facebook, already started this process. CSDS will make it efficient.
With our team of software and knowledge consultants we plan to bring additional 10% of revenue.
We plan to open our educational
platform to IT and other selected industries and pay selected students and
SMEs to become instructors and marketing partners. After initial investment,
this program will become profitable, although we plan to keep low profit to
Our goal is to shift gears in higher education towards companies, expanding education beyond Academy,
helping directly connect Job Market and students, greatly improving educational results.
In several years this area of business might bring
significant attention and profit.
Questions and Answers:
What is the
actual goal and problem space you are looking to address?
The goal is trivial although the solution is not. The goal is to
let computers do things that are mostly tedious operations, but today are
performed by people. We often call these people “knowledge workers” as they
operate with information. This work can be more automated, saving trillions of
dollars. To enter this market means to teach computers to understand more and
to help more.
Why your solution? Are there others in this problem
space? What differentiates you from others?
There are three major solutions in this space.
All three solutions help computers understand more and help more.
a) Generic search by Google – helps all "knowledge workers" regardless of their profession
search and find information on the web via entering related keywords. This
saves about 10% of time/budget, which is about $600 billion dollars (a total
market is about $6 trillion dollars).
b) Engaging knowledge engineering consultants to create specific
knowledge domain ontologies by using Semantic Tools, such and AllegroGraph, TopBraid, OntoText
- Creating Ontology is a very hard task, which requires both
domain expertise and knowledge engineering expertise. Plus it is a very time
consuming task. These challenges limit success of this movement and these tools
to about 0.5% of market.
c) Cognitive computing, when we throw data in the computer,
giving a good algorithm and a chance to figure out what is these data about and
find data patterns for analysis.
- This is a relatively new technology, like IBM Watson, which has
a long way for success.
- The biggest challenge is what to do with the patterns found?
Computers, while looking for patterns, currently is not aware about business
goals and specific applications where these patterns can apply.
- Overtime, I expect cognitive computing to be integrated with the
conversational approach described in several patents.
Our solution combines computer restlessness with a domain expert
intuition via conversational interface. It is a system and methodology, which includes
components described before and adds a conversational mechanism
to better connect SMEs, ontology, and a computer programs.
Is your solution a better way?
Our solution, which is protected by several patterns, combines
computer restlessness with a domain expert intuition via conversational
The system includes original conversational scripts prepared once
by a consultant working together with a domain expert.<
Then the system uses the scripts to grow knowledge domain ontology
and uses the growing ontology to collect knowledge domain information from data
sources. Each interaction with users enhance the conversational scripts, grow
knowledge domain ontology and makes more precise definition for collecting data
from public sources, which in its own turn improves ontology.
Growing Ontology allows business users to gain immediate
advantages in their business processes and with every interaction help growing
At some point a growing ontology can help more complex
applications, such as:
- Conversational Design and Manufacturing,
- Creating alternative Education by helping SMEs transfer their
knowledge into educational materials, and more.
There are several companies, such as Moxie, eGain,
and MindTouch that describe similar ideas.
too small to spend any resources on fighting patent rights. At some point a
bigger company will buy our patents to enter this game. This happened with our
patent, US 7032006, Distributed
active knowledge and process, which was obtained by Yahoo.
But while these companies use "Guided Search" (can be considered
as a conversation) none of these companies close the loop. In our case every
interaction with users and every new bit of information in knowledge ontology
makes improvements in all system components. Conversational scripts not only
help users, but also help growing knowledge domain ontology, enhance
conversational scripts, makes more precise definition for collecting data from
public sources, which in its own turn improves ontology.
What is the technology, in regular laymen terms?
Is this a form of AI, a new methodology or both?
- It is more a system and methodology that uses lower forms of AI,
such as ontology and semantic tools
Do you have a working prototype? Does it clearly
demonstrate the technology?
Yes, we have a prototype, Business Architecture Sandbox for Enterprise
(BASE) described in the book http://ITofTheFuture.com
. BASE currently
supports online study at ITU (http://JavaSchool.com
) and serves as
a playground for new technologies. My estimate for expanding BASE to full scale
product is about 6 months of work fulltime with a small team (two or three people;
never had this opportunity to focus fulltime on this work).
Feel free to ask more questions...
Jeff (Yefim) Zhuk | 720-299-4701 | email@example.com
© 1995-present by ITS/ITU, Inc, all rights reserved. US Patents:
Distributed Active Knowledge,
Knowledge-Driven Architecture, Adaptive Robot Systems, Rules Collector, Collaborative Security and Decisions