The document analyzes the impact of a business plan for an AI-based troubleshooting system developed by the author in 2000. It provides background on the author's research experience in AI since 1989. The 2000 business plan proposed using AI for automated troubleshooting, maintenance, and diagnostics for internal technical support of enterprise applications. It highlighted application to software debugging and business functions. An analysis finds similarities between concepts in the 2000 plan and later AI trends, suggesting it was ahead of its time. The document conducts an impact assessment to analyze the value of the author's early work in the field.
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Ai troubleshooting impact analysis 2017
1. ARTIFICIAL INTELLIGENCE- BASED
TROUBLESHOOTING
IMPACT ANALYSIS OF THE
BUSINESS PLAN (2000)
By Shubhadha Iyer
August 26, 2017
This document presents an overview of the consultant Shubhadha Iyer’s research/academic
work experience and independent Thought Leadership initiatives on the subject of “Artificial
Intelligence-enabled Automated Troubleshooting & Diagnostics” during 1989-2002 and later, in
2013. It provides the background and details on the Business Plan initiated by the consultant,
March-April 2002 in USA. Based on a methodology devised by the Consultant a formal Impact
Analysis is then conducted to analyse the value-add from the consultant’s activity in 2000, USA.
This document is for Free distribution
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TABLES
Table 1 Activity Timelines for the AI Software for Troubleshooting Business Plan (2000)................................................................... 8
Table 2 Impact Assessment Matrix – AI Software for Troubleshooting (2000) – Metadata................................................................. 9
Table 3 Impact Assessment Matrix – AI Software for Troubleshooting (2000) – Points of Similarity ................................................ 10
Table 4 List of US Patents Filed until year 2000-2002 (phase 1)......................................................................................................... 13
Table 5 List of US Patents Filed until year 2000-2002 (phase 2)......................................................................................................... 14
Table 6 Proof of Activity...................................................................................................................................................................... 19
FIGURES
Figure 1 Online view of AI for Troubleshooting Plan/Paper published May 2009.............................................................................. 17
Figure 2 Existing Yahoo Groups “SIFILES” (login required) Files with the “AI for Troubleshooting” B Plan (posted 2002) ................ 17
Figure 3 Excerpt from Consultant S.Iyer’s E-Book............................................................................................................................... 17
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DISCLAIMERS, TERMS & CONDITIONS
The consultant is happy to note that the Business Plan/Project Proposal “Automated
Troubleshooting, Maintenance and Diagnostics for Internal Technical Support...” independently
initiated and prepared in year 2000 in USA; also referred to here as the “AI for Troubleshooting
(2000)” Business Plan– supported and contributed to the field of Artificial Intelligence.
1. The current document aims to highlight the main points of the B Plan or Project Proposal prepared
by the “consultant” Shubhadha Iyer (“author” of this document, also referred to as “S.Iyer”)
2. For the purpose of Impact Analysis, a comparative analysis of similar works was essential to this
document. Importance was given to the time frame of past events and a background study of the
market landscape.
3. In doing so, there is no attempt to disturb, interfere or cause inconvenience to any of the existing
products/systems in this domain, or to raise any issues concerning the early or later works.
4. Based on information herein, the consultant does claim to have initiated and prepared work on the
above subject, at a given time in advance of other events that took place. The consultant also
claims this work was similar in some aspects, to later works in USA and India.
5. This information cannot be used as commentary or feedback on any of the products/ systems or
events being compared with or discussed in this document.
6. This information CAN only be used to factually verify and to endorse the consultant’s claims
regarding her own work of thought leadership.
7. The consultant can provide verifiable proof of this work in the form of offline & online documents
and other material as mentioned herein, including authentic archived email records.
8. The consultant sent emails to Entrepreneurs/ VC networks and Forums in USA to promote the
concerned work-- this can be verified without disclosing confidential or personal information. This
Proof exists for verification of-- the date/time of the consultant’s work; and the macro-level
analysis performed by S.Iyer to arrive at this solution, viewable in the text of her emails.
9. Consultant S. Iyer did not receive any form of support or guidance in course of this work that she
alone initiated, wrote and completed. An online/freelance USA resource was contracted by S.Iyer
for the purpose of adding a few paragraphs of promotional brochure-type information.
10. The aim of promoting this B Plan in USA (2000-2002) was to demonstrate S.Iyer’s competencies for
high-level consulting in terms of—macro-level strategic analysis, innovative thinking and grasp of
market trends in AI. The consultant sought professional contacts as a step toward locating future
project opportunities—i.e. AI projects and other high-level consulting for USA organizations.
11. The consultant S. Iyer also showed this work later to corporate authorities (2003 to 2006) with the
same objective—i.e. to be considered for USA-related project work onsite. And the consultant
received positive feedback for this work. However, NO project opportunities have come up so far.
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INTRODUCTION
“Artificial Intelligence (AI)”-- defined as the “capability to apply human-like or higher levels of
intelligence” that involves cognitive activity- learning, remembering, reasoning (and in some cases,
sensory perception) for the purpose of problem-solving and/or decision-making.
A historical view shows AI software being developed within academic Institutions and R&D
projects. And these solutions were being applied in selected mission-critical verticals. To some
extent companies in the 1990's had integrated Expert System (ES) functionality in business
functions. Then from 2000 to 2010 we see a rather slow and gradual growth of investor interest
with commercial application of AI in places. But in general, the movement in AI remained subdued
compared to other areas in IT/software and Technology.
It is only in recent years, after 2010—that we see a dramatic rise in popularity and corporate-
sponsored investment for AI-enabled automation. Now that ancillary tools/systems to support AI
are in place, the core AI technology is finally “happening” like a tidal wave-- and industries are
getting ready to meet the rising demand for intelligent software technology. AI is all set to be the
new “fad” -- widely written about in the media; but also in a position to set some serious new
paradigms!
Of course, the term “Artificial Intelligence” is a vast field involving different types of specialized
software (and hardware). Currently, we see AI evolving as part of-- Machine Learning, Robotics,
Process Automation, Data Science and Analytics, Auto Navigation, Speech Recognition, and
Customer-centric Virtual Assistants among others. Very soon, it will become difficult to simply
generalize AI as “AI” (no doubt, a sign of its practical adoption in the real world).
Now this document will focus on an important application of AI software—the one meant for
automated “Troubleshooting and Diagnostics”. A fantastic challenge (even by today’s standards) --
the author had initially researched the subject in 1989 (India); studied further in 1997 (USA); then
independently analyzed and wrote about it since year 2000 while in USA. Given such a clear head
start of 8-10 years before AI hit the next popularity wave (that we estimate started around 2010) --
It would have been nice to claim “AI for Automated Troubleshooting” as a first, but we cannot.
That’s because AI for Troubleshooting was working unobtrusively at nuts-and-bolts level, even
back in the 1980’s and 1990’s. AI in the form of “Expert Systems (ES)” was helping Aircraft
Engineers and Mechanics track failures; AI/ES was being applied to diagnose Medical problems; and
AI tools sat on a few computers assisting IT with Circuits and Network Management! There are
hundreds of patents and far-sighted research publications writing extensively about the potential
applications of AI including AI’s major role in fault/ medical diagnosis over the years.
A continuing challenge is to further develop AI-based analysis and troubleshooting to work in
IT/Software and Business environments. The author will now proceed to claim a few points for her
own thought leadership initiatives on this subject in 2000-2002 -- based on verifiable facts detailed
in this document.
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AI TROUBLESHOOTING: THE PRIOR WORKS: UNTIL 2000
1. The consultant S. Iyer has conducted multiple searches for US Patents on the topic of “Artificial
Intelligence for Troubleshooting”, “Troubleshooting Expert Systems” and related concepts filed
until and including year 2000. A search for Journal publications on the above topic was also
conducted.
2. Prior Patents and Prior Publications –selection of Patents and Journal publications considered
to represent early works on the subject of AI software for troubleshooting until 2000.
CONSULTANT’S RESEARCH & ACADEMIC BACKGROUND IN AI: 1989-90, 1997
Consultant S.Iyer initiated or supported the following activities in the field of AI software:-
1. After completion of B.Tech in 1988 the Consultant participated in a Research project (1989-90)
at a reputed academic Institute. The project involved Fault-Tracking for Aircraft based on
Diagnostic Expert Systems technology.
2. The author’s assignments included preparation of Fault Tree Charts to model the fault analysis
of some common problems in an Aircraft Engine (as a step toward automated diagnosis).
3. Later in course of M.S. Engineering in USA the author/consultant became more involved in
IT/Software programming and revived her interest in Expert Systems (LISP programming and CS
concepts) by taking an additional CS course in 1997. And continued to think about where all the
AI/ES technology could be applied in the IT/software industry.
4. In year 2000, concluded that ES and in general, AI could work well for application-level
troubleshooting, knowledge transfer and other areas in mainstream business environments,
given the rising levels of complexity in MIS/ Business applications.
5. Estimating the design for this AI product to be quite different from previous applications-- and
eager to develop an innovative prototype/ product design, the consultant prepared an early-
stage Business Plan/Proposal in 2000 and submitted the plan to Silicon Valley VCs/investors.
6. The Plan (2000) received positive feedback from the business community and was invited for
presentation at an Entrepreneur forum. But micro-level funding was not available; and due to
project work commitments the consultant could not afford to spend more time on pitches.
7. From 2003 to 2007, S.Iyer continued to promote the idea to an Indo-US Entrepreneur network,
formally presenting the Plan to an Indo-US VC Firm in Bangalore, India (2007). But again due to
work commitments, could not afford to prepare the prototype without any funding.
8. Nevertheless, interested to take up AI-related project opportunities from companies in this or
other domains- the consultant published the Plan/Proposal online from 2009 to 2011.
9. Continued to track industry trends on AI Troubleshooting, Diagnostics and Decision Support. In
2013, wrote further on “Intelligent Software Agent (ISA)” technology, exploring its implications
for Management. Refer this excerpt from the consultant’s book “Ideas in People Management
(2013)” (Click on Preview under book image)
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AI TROUBLESHOOTING: AUTHOR’S BUSINESS PLAN IN USA: 2000
A Business Plan/Project Proposal titled “Automated Troubleshooting, Maintenance and Diagnostics
for Internal Technical Support” was written and promoted by Shubhadha Iyer in Silicon Valley, 2000.
Promotions to the Entrepreneurs and Corporate world continued from 2000 to 2007. Between
2009 and 2011 the author published this paper online on a number of sites.
The Business Plan’s (2000) key differentiators --
AREAS OF APPLICATION Suggested Diagnostic AI for Software Application-level Debugging and
Maintenance thereby extending the value of AI-based Troubleshooting from “machine parts”—
to work as part of Internal Tech support of Enterprise applications and Business functions.
BUSINESS FUNCTIONS Extended AI toward more Business-centric areas --e.g. to automate
Knowledge Transfer, Administrative and Escalation procedures, and Report generation within a
company. Promoted AI for improving Operations, Business Process and Workflow.
INDUSTRY DOMAINS Promoted the benefits of AI-enabled automation for “e-Business”
environments and those with busy application support workloads and training needs– such as
Call Centers, Startups, Web-based Retail, Manufacturing and IT.
AI AND RULE BASED CONCEPTS Popularized and marketed important IT/Software Tools and
Concepts in 2000, as the pre requisites for AI– e.g. “Diagnostic procedures” “Knowledge Based
Systems”, “Rule Based engine”. And in general, raised awareness of AI concepts.
A high level of strategic alignment may be noted between the above concepts suggested or
promoted in the Business Plan (2000-2002)--and IT/software trends of the past decade (2000-
2010); more especially with recent trends in AI that started after 2010. Further detailed information
on the above findings is presented here with a point-wise impact analysis.
GROWTH OF AI IN INDUSTRY: FROM 2000 UNTIL 2010
In the years following 2000, we find sustained investor interest with some AI software companies
being recognized by Silicon Valley investors, Entrepreneur and VC Networks.
From 2000 to 2010, we find a proliferation of Decision Support tools as part of commercial
applications in USA. E.g. -- Rule-Based systems, Knowledge Base for KM and Troubleshooting,
Fault Tree Charts in MS Office. Fault Tree Analysis (FTA) is now a part of desktop software; and
Knowledge bases for IT support (Help desk) have seen adoption on a larger scale.
Startups that apply AI including Expert System (ES) have come up in USA and India (observed
2007-2008 onwards till date).
GROWTH OF AI IN INDUSTRY: 2010 TO PRESENT
By 2012, the AI game picked up speed. Big IT MNC’s entered the game with their own AI-based
products and platforms- strategically applying “AI software for IT/Business Support”, and
“debugging” as part of IT support (Helpdesk) functions. Essentially, driven by the same principles
the consultant’s 2000 Plan had promoted.
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1. IBM Watson and Wipro Holmes—widely written about in newspapers/ online articles,
heralded the rapidly growing awareness and popularity of AI in mainstream corporate and
business environments – 2010 onwards.
2. Applications of Diagnostic AI have grown in number. One of the areas that Holmes (2016)
applies AI for is-- “problem resolution” in IT/Software and Help desk environments aiming to
reduce the workload on IT Support personnel or “internal Technical teams”.
3. One of the problem-resolution approaches adopted by the “cognitive computing” AI
systems is to detect patterns in big data including defects and “trouble ticket” logs, working
with predictive analytics and “learning” from previous outcomes.
4. In recent years we see a growing number of AI-powered business and customer-centric
applications. There is currently a surge in Administrative automation through AI.
5. AI is now a major investment focus—as per online article, “Funding for AI in 2016—between
$26 billion and $39 billion—is about triple the amount of three years ago”.
THE BUSINESS PLAN FOR AUTOMATED TROUBLESHOOTING WITH AI SOFTWARE (2000)
BUSINESS PLAN VISION & STRUCTURE
The Business Plan (2000) surveyed different forms of AI-enabled troubleshooting that already existed.
Based on Market analysis, the author promoted some pivotal AI concepts for the benefit of Investors/
VCs in USA, suggesting innovative strategies to extend the role of AI in business; stressing the need to
automate the diagnostic “thought route” and in general, raising awareness of Diagnostic and Decision
Support requirements.
1. The Project Proposal (2000) titled “Automated Troubleshooting, Maintenance & Diagnostics for
Internal Technical Support..” proposed the development of Automated troubleshooting solutions
based on Artificial Intelligence (AI)—i.e. Troubleshooting Expert Systems (ES) but not limited to this.
2. The thematic Overview section argued for AI Tools to enable application support, maintenance and
problem resolution in the organization. It was noted that business environments were increasingly
web-centric, multi-tiered and distributed with so many disparate applications to support in a
company, that the level of complexity approached that of Aircraft maintenance.
3. Salient Features outlined a High-level System Design of an Expert System Shell—with interactive
GUI frontend to represent the rules, Rule database, Knowledge Base and Engine.
4. Market Analysis observed the growing need for improved diagnostics and maintenance support
requirements within the organization. Key product differentiators to enable a better managed
Enterprise were identified as-- improved methods to maintain rules, diagnostic and escalation
procedures in the company.
5. Estimated that AI-based products would fill a critical niche in “e-Business” environments. Target
Markets included companies with busy application support workloads –such as Call Centers,
Startups, Web-based Retail, Administrative, Manufacturing and IT— Enterprises having a “chain”
of high-maintenance platforms/applications to check and analyze.
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6. An Operating Plan, Sales Forecast and Projections were outlined. Projections estimated that even in
the worst case the AI software assets could be leveraged for non-critical functions such as—
customer survey, and Report generation.
BUSINESS PLAN HIGHLIGHTS
The Project Proposal for “Automated Troubleshooting, Maintenance and Diagnostics for Internal Technical Support” (2000)
served the following functions-
The AI Troubleshooting Plan (2000) prepared by Shubhadha Iyer is currently viewable at :
http://www.siyerconsult.com/files/documents/AI-Based-Troubleshooting-Diagnostics.pdf
1. The Plan identified the following Business needs and key challenges for IT/business environments:-
a. Need to analyze, maintain and diagnose multiple sub-systems, platforms and hardware.
b. Need to automate operations, administrative and escalation procedures as part of problem
resolution and routine maintenance checks within a company.
c. Need for more efficient and cost-effective “Knowledge Transfer from experienced Support
personnel to new hires” on complex repetitive procedures.
d. Need for diagnostic tools with intuitive frontend interface.
2. Envisioned the extended role of Artificial Intelligence software in the business environment:-
a. AI for “internal technical support”, “Application debugging”, “internal support tasks” for
IT/Software Applications and “resolving issues in companies”.
a. AI as a powerful troubleshooting tool-- for software systems/ applications and also at
“operations” and process level “in the mainstream corporate environment” (versus the
previous/ limited role of AI as a basic debugging tool for machine parts, etc.).
b. AI for “Technical and Administrative issues resolution, including Escalation and emergency
procedures in the company”.
c. AI for “generating executive-decision Reports”.
d. AI and Rule driven capabilities for improved “Workflow analysis” and process automation.
3. Promoted/popularized the following concepts and tools in year 2000-2002, USA-
a. Expert Systems (and in general, AI concepts) for automated Troubleshooting and related
functions –e.g. Knowledge Transfer in IT/Business environments.
b. Automation of Diagnostic procedures-- i.e. the diagnostic “thought route”
c. Knowledge Base as a cumulative base of rules and procedures to “imbibe new problems as
they arise”
d. Rule Based System with frontend interface
e. Fault Tree Charts
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ANALYSIS OF PRIOR PATENTS IN USA & RESEARCH PUBLICATIONS (UNTIL 2000)
To proceed with our accurate assessment of the thought leadership value of S. Iyer’s Plan/Proposal
(2000), the following observations are made based on prior works (patents and publications until year
2000) on the subject of “AI software for Troubleshooting” and related ideas/concepts):-
SEARCH FINDINGS
1. Application of “AI for Diagnosis” or “Troubleshooting Expert Systems” was known and prevalent in
the 1980’s- 1990’s albeit limited to fault-tracking in specialized Technical and Medical domains as
supported by Patents, publications and other sources of R&D information (until 2000):-
Engineering (Auto/ Machine parts, etc.), Computer systems, Electronic Circuits/ Devices and
Network management.
Medical Diagnosis
2. While there are AI patents associated with a specific domain e.g. Circuits, a few other patents
indicate generic application for a given design—E.g. Patent 4,866, 635 (1989) forwarded a domain-
independent diagnostic AI/ ES shell “that can be used to build a Diagnostic Expert System”.
3. A number of Publications (until 2000) consider AI or Expert Systems (different types than
diagnostic— planning, scheduling, design) to provide business planning and decision support in
“business domains” (E.g. Real estate forecasting, finance, corporate tax planning, HRM).
4. In terms of areas of application-- a number of patents and publications associated AI with–
operations (operation-level Expert System), process modeling (business & manufacturing), process
diagnostics, workflow, MIS Support for planning, Knowledge Management and other functions.
5. Computer program debugging –was another area of application considered for AI. Case-based
Reasoning (CBR) for intelligent “Help desk” support appears as the subject of numerous
publications.
Refer Prior Patents and Prior Publications for a selection of these patents and publications.
COMPARISON OF SEARCH FINDINGS TO PLAN/PROPOSAL (2000)
Academic/ Research Publications until 2000 considered AI for a wide spectrum of applications from
planning and decision support to High-Tech. However (we generalize) -- the role of diagnostic AI to
support business functions—compared to medical and technical fault diagnosis-- was limited; and we
proceed to make the following observations with specific reference to the Plan/ proposal (2000) -
1. We did not find a major focus involving AI-based Troubleshooting for some techno-functional areas
pointed out by the Plan—i.e. for “Application Debugging”, “Application support”, “issues resolution”
and “internal support tasks” for IT/Software Applications in Business environments.
a. Automated troubleshooting and maintenance of IT/Software Applications is a distinctly
challenging area with—(let’s face it!) woefully under-developed real-world implementation.
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b. IT/Software Application Support often involves multiple levels of diagnosis from code to
systems and process. And “Issues resolution” might span multiple applications and
process—often approaching the level of complexity in Technical components. Noting this
challenge, the consultant had emphasized this area of application in the Plan (2000).
2. AI-based automation of “Emergency procedures” was considered in publications; with limited
references to “Administrative procedures”. But not finding significant prior instances of AI-based
Administrative automation in business/corporate environments--- this area was also promoted.
3. The Plan identified certain domains with high application support workloads for this type of AI/ES.
4. It can be argued that a “domain-independent Expert System shell” would implicitly work for any
domain -- as fundamental aspects of a stable computing system architecture remain the same
whether machine parts or Enterprise software—i.e. the ES shell is domain-independent.
a. Agreed—But even with the same ES Shell design, there would be working differences
between an AI system for checking Electronic devices or Circuits and one that checks
Software systems/applications—e.g. in the levels of diagnostic reasoning/logic involved—
and this would determine the diagnostic strategy and methodologies to be adopted.
b. Domain and function-specific differences can be expected to impact overall product vision
and strategy–where it concerns AI system configuration, implementation, interface/
integration requirements, supporting tools/components and of course—the “knowledge
representation” (knowledge schema for Faults/Cases and the domain-specific models).
c. Furthermore, an AI system for complex application-level diagnosis may well require a
combination of several “cognitive information processing” methods that a basic “Circuit
tester” will not.
5. Hence, as part of the solution the Plan/proposal (2000) did emphasize several viable areas of
application and domains (knowledge and IT/software intensive) with a view to setting a robust long-
term vision, concept and strategy for applying the diagnostic AI—one that could work on Software
applications, systems, procedures, business process and Decision Support.
OBSERVATIONS REGARDING THE PLAN/PROPOSAL (2000)
In summary, we observe that— traditionally (until 2000) Diagnostic AI or Expert Systems technology for
enabling higher levels of “business automation”—had seen limited application compared to the
specialized technical domains.
In contrast, AI for Application support was the focus of the Plan/proposal (2000) which argued for
Troubleshooting at the level of IT/Software Applications; for automating organizational procedures
and other methods of extending AI/ES in corporate/business environments – a point in favor of
novelty.
By further considering AI for automating procedures, knowledge transfer and other business
functions-- a multi-functional “organization-wide” AI system was being considered in the
Plan/proposal, for providing integrated Information Management and MIS Support.
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OBSERVATIONS (CONT’D)
While AI was known for code-level i.e. “program debugging” the Plan concept suggested that-- even
IT/ Business applications and process could be modeled for higher levels of automated diagnosis
and maintenance. The system could then support Enterprise business functions including
administrative, escalation and operating procedures.
Expert Systems (ES) have long been recognized as an important value-add for Knowledge
Management. In this context the Plan/proposal forwarded insightful observations-- promoting ES
Technology for Knowledge Transfer and training business environments.
Such an AI system would accommodate multiple diagnostic approach & methods for Enterprise
applications; and could also be leveraged for managing business functions, rather than just be a
“Troubleshooting ES”.
IMPACT ASSESSMENT – ACTIVITY TIMELINE
An Impact assessment is now conducted, incorporating the prior work search results. We start with the
Time series of events related to the AI for Troubleshooting Business Plan/Proposal (2000).
Table 1 Activity Timelines for the AI Software for Troubleshooting Business Plan (2000)
Activity measured for Impact Timeline
Main
Activity
Preparation of the Business Plan titled “Automated Troubleshooting,
Diagnostics and Maintenance for Internal Technical Support” in CA, USA
Sep 2000
ActivityMilestones
The Plan was promoted to the Business community (Entrepreneur
Networks and VC forums) in USA
2000 - 2002
The Plan was included for presentation at the “Inv-Ent” forum in USA 2000-2001
The initiator (S. Iyer) continued promoting this plan to Entrepreneur
networks and corporate authorities.
2003- 2007
Formally presented the plan to an Indo-US VC Firm in Bangalore, India
Was asked for a prototype/demo but funding and time constraints
precluded work on prototype preparation.
Jun 2007
S. Iyer published the Plan/Paper online on several sites 2009 - 2011
Currently viewable on a few sites with online publication dates. May 2009,
Sep 2011
13. 9 | P a g e A I T r o u b l e s h o o t i n g I m p a c t A n a l y s i s S. Iyer
IMPACT ASSESSMENT MATRIX
Table 2 Impact Assessment Matrix – AI Software for Troubleshooting (2000) – Metadata
Measure Value Explanation Impact
TL
value
- “Thought Leadership Activity” refers to the Business Plan prepared for Automated Troubleshooting with AI (USA, Sep 2000).
- “Commercial application” (“Event”) refers to AI applications in industry that apply the same Technology, Function or Concept
(USA & India, 2000 - 2017). Here, “Function” or “Concept” refers to any software/devices providing the same functionality and/or
working in the same area of application proposed in the above Plan.
1
Time span between this thought leadership
activity and one or more major commercial
application(s) that followed
8-10 years
Thought Leadership activity involving the same
concepts with an early “head start” (> 7 years)
is considered relatively high in terms of “lead”
High
Example of a commercial deployment– Holmes (2016) applies diagnostic AI as one of the solutions. Though the exact Tech/ design/
algorithms are not known to us, this AI platform performs “problem resolution” in IT/Software Help desk environments aiming to
reduce workload for “Internal Technical support”, processing service/ trouble tickets and “learning” from previous outcomes.
Hence, we find certain similarities between this application (Holmes) and the concept and areas of application outlined in the
preceding Thought Leadership work (2000)—the B Plan, which proposed knowledge-based diagnostic AI for problem and issues
resolution in software/ business environments including Call Centers and as a debugging tool for internal IT/ Application Support.
2
Number of prior Journal Publications that
mention “AI” with “Internal Tech Support” or
“Application Debugging” from 1975- 2000 / No. of
online Journal Publications in the same area
AFTER 2000 - present (source: Google Scholar)
17/107
From online research we estimated a count of
prior works—Journal publications that
considered AI for Internal Technical Support or
Application Debugging until vs. after 2000
High High
3
Number of US Patents (estimated) involving the
same or similar concept as the B Plan (i.e. AI or
Expert Systems for troubleshooting or diagnosis
and applied for the same type of environments
proposed in the B. plan -- IT/ Software Application
support/ Business applications) UNTIL 2000
(Source: Google Patents for US Patents, USPTO
Online & other US Patent sites)
- Patent search was conducted in two phases
(Refer Prior Patents) from which patents
were further selected based on a series of
search refinements to focus on the area.
10
Initial search displayed patents involving AI/ES
technology in troubleshooting or diagnosis.
Patents were concerned with various aspects
of generic AI/ES design OR applied diagnostic
AI for selected domains.
- We considered a subset of the above
relevant in the context of -- IT/Software
application support and debugging,
technical support or help desk, issues
resolution, and Business applications. Refer
Prior Patents for the selected list (sample).
Medium Medium
4
Activity Differentiators: Methods used to present
or promote this Thought Leadership activity(AI
Software B Plan prepared in 2000)
Online/
Emails to
networks/
Business
forums
Idea/concept was actively promoted in Project
Proposal format. Emails were sent to high-level
USA Entrepreneurs/VC Network with 100+
members in 2000-2002, and in 2007- officially
presented to an Indo-US VC Firm and by 2010,
also published online.
High
5
View/Read count of the submitted plan/ proposal
and online publications of the AI Software
Business Plan/Proposal (2000)
10+
In addition to this verifiable read count of the
submitted Plan, the View count on consultant’s
White Paper/ Consulting website which has
hosted the Plan since 2011 is 20,000+
Low
6 Funding/ sponsorship for this Activity None This was an independent initiative. Low
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IMPACT ASSESSMENT MATRIX (POINTS OF SIMILARITY)
Table 3 Impact Assessment Matrix – AI Software for Troubleshooting (2000) – Points of Similarity
Table Measure Explanation
8
Points of Similarity observed between Ideas/
Concepts or Features promoted by the Thought
Leadership Activity (AI Software Plan 2000) and the
successful commercial AI applications being
considered (2010 onwards)
This involves a comparison between the originally proposed concept and
later commercial application(s) that followed. Each key concept/feature is
evaluated based on Similarity and Novelty (newness). The final Impact and
TL value are rated based on the ‘New’ and ‘Similar’ values.
Idea/ Concept/ Feature being Compared Value Explanation Impact
TL
value
(i)
Tools and Technology: - The B Plan (2000) proposed a
solution based on AI/ Expert Systems requiring Rules
engine, user interface, and Knowledge Base
architecture.
New: Low
Similar: High
Diagnostic AI Technology/ tools were
known in specialized domains hence the
‘New’ rating is ‘Low’. These tech/tools were
used in later applications in industry hence
concept similarity is ‘High’. Ultimately,
Impact of the B Plan concepts and solution
is considered ‘High’ as general awareness
of AI and ES technology in year 2000 was
low-- the B Plan was in a position to raise
awareness for the benefit of later works.
High Low
(ii)
The B Plan (2000) proposed Knowledge-based AI for IT
Application support and issues resolution in Business
environments including Web-based, and Call Centers.
New: High
Similar: High
Prior works do not indicate a major focus
on diagnostic AI applied for IT/ Business
application support or issues resolution. It
is only in recent years that AI has seen a
wave of implementation in IT Support,
helpdesk and related areas of business.
High High
(iii)
The B Plan (2000) proposed AI for Administrative
procedures in the business environment
New: High
Similar: High
AI-enabled Administrative support was not
widely applied before in industry, hence
New is ‘High’. Recent years have seen
major growth in administrative automation.
The B Plan promoted this concept hence TL
value and impact are considered ‘High’.
High High
(iv)
The B Plan (2000) linked Rule-driven AI to workflow
analysis, process automation and operations.
New: Low
Similar: High
A few prior works did consider AI for
operations, workflow and process (New
=Low). This suggestion was forwarded in
the B Plan (2000) but without detailed
analysis hence we consider ‘low’ to
‘medium’ TL and impact.
Medium Low
(v)
The B Plan (2000) proposed AI for Escalation and
emergency procedures
New: Low
Similar: High
This utility of AI was previously known for
emergency procedures hence not “New”.
But the B Plan possibly had an impact in
raising awareness/ popularity.
Medium Low
(vi) The B Plan (2000) proposed AI for Report generation.
New: Low
Similar: High
Intelligent/ Knowledge-based Report
generation was a previously known
application of AI. It has grown with Decision
Support technology and expected to grow
further with Natural Language Processing.
Medium Low
16. 12 | P a g e A I T r o u b l e s h o o t i n g I m p a c t A n a l y s i s
CONCLUSIONS ON IMPACT ANALYSIS OF THE BUSINESS PLAN (2000)
Based on this study we may conclude that--
In terms of Impact, the Business Plan/Proposal (2000) pointed out innovative ways to extend AI --
beyond the High-Tech silos in Engineering, Medical or Electronic Circuits/ Networks, extrapolating its
benefits for resolving problems in Business environments. This vision and strategy for applying AI in
business and customer-centric areas was accurate -- as seen by the successful forays of recent AI-related
ventures in the USA and Indo-US markets. The plan also raised awareness of AI technology and
popularized certain futuristic high-growth areas associated with AI.
SCOPE OF THIS STUDY
For this study we primarily focused on US patents and worldwide journal publications, News and online
articles to assess the activity levels in this domain.
POINTS TO NOTE
This Business Plan/proposal (2000) was prepared by Shubhadha Iyer as an early stage/ Concept note to
drive project strategy and vision. The aim was to develop a proof-of-concept with detailed working product
design and functionality that the consultant had in mind (but had not specified in the high-level B Plan!).
The Proof-of-Concept was held up for lack of timely micro-level funding and other constraints faced by
the consultant. Nevertheless, S. Iyer still has ideas to develop this concept and wants to take it up as a
future project, if given the opportunity.
We admit the Plan/proposal had some limitations-- the methodology restricted itself to FTA. The high-
level design was not meant to be detailed in capturing the full “cognitive computing” technology or
methodology requirements that Application-level troubleshooting will demand.
While citing “similarity” we do not claim that later applications worked the same way as envisioned in
the Plan/proposal. Quite possibly, the exact solution proposed in the plan for application-level
troubleshooting-- has not yet been applied anywhere!
An industry survey would readily show that -- we are nowhere near to achieving full utilization of AI’s
potential for troubleshooting—a function requiring different types of knowledge, reasoning and analysis
even for resolving seemingly simple issues! Application Troubleshooting still offers uncharted territory
with vast scope for AI solutions. Related applications envisioned in the plan –such as operations,
procedures – are also worth developing further through AI.
We conclude this Impact analysis on the note that- Almost two decades ago, a Business Plan was written
and promoted so well ahead of the current “AI Wave” that it was in a position to accelerate some
aspects of the AI thought process. In the past decade many more AI initiatives have come up; and S.Iyer
is happy to have participated in promoting AI concepts and applications--way before that!
But Nobody Owns AI! And now the initiator of that Plan wants to contribute again, adding ideas and
solutions that work with the current level of advancement in Artificial Intelligence.
17. 13 | P a g e A I T r o u b l e s h o o t i n g I m p a c t A n a l y s i s
PRIOR PATENTS
US Patent Search Results (phase 1)
The following search results represent “prior works” on the subject of “Artificial Intelligence for Troubleshooting or
Diagnosis”--ahead of the Business Plan prepared by S.Iyer titled “Automated Troubleshooting, Maintenance and
Diagnostics for Internal Technical Support” (Sep 2000). This Prior art search (phase 1) was conducted in 2015 with a
combination of one or more key words relating to the invention as specified below.
Source: - Prior works from multiple sources - US Granted Patents and US Patent Publication published online by
USPTO, EP Granted Patents, EP Published Patent Applications and WIPO PCT Publications. Further, a Classification
Search was conducted in the online patent database (USPTO). Note: - This is a selection of patents from the
search results and not a complete listing.
Key-phrases/Criteria applied for Search (Phase 1):- (For patents granted or filed until 2000) AI expert system for
troubleshooting, Expert system for business process automation, AI based business process automation, AI based
IT issue resolution, Artificial Intelligence for Troubleshooting or Diagnostics, Expert System for Troubleshooting or
Diagnostics, Troubleshooting in IT or Business, Knowledge Based AI, Fault Tree Analysis
Table 4 List of US Patents Filed until year 2000-2002 (phase 1)
Date of Patent US Patent No. Patent Description Contributor(s)
Sep 12
th
, 1989 US4866635A DOMAIN INDEPENDENT SHELL FOR BUILDING A
DIAGNOSTIC EXPERT SYSTEM
Gary S. Kahn, Jeffrey A. Pepper,
Al N. Kepner, William Richer,
Rajiv Enand
Apr 21
st
, 1992 US5107499A Arrangement for Automated Troubleshooting using
Selective Advice and a Learning Knowledge Base
Yuval Lirov, Swaminathan
Ravikumar, On-Ching Yue
Apr 21
st
, 1992 US5107497A TECHNIQUE FOR PRODUCING AN EXPERT SYSTEM FOR
SYSTEM FAULT DIAGNOSIS
Yuval Lirov, On-Ching Yue
May 2
nd
, 1995 US5412756A Artificial intelligence software shell for plant operation
simulation
Douglas A. Bauman, Simon
Lowenfeld , Brian A. Schultz;
Robert W. Thompson, Jr.
Jan 9
th
, 1996 US5483637A EXPERT BASED SYSTEM AND METHOD FOR MANAGING
ERROR EVENTS IN A LOCAL AREA NETWORK
Alex Winokur, Joseph Shiloach,
Amnon Ribak, Yuangene Huang
Feb 3
rd
, 1998 US5715371A Personal Computer-based Intelligent Networks Syed Vickar Ahamed, Victor
Berrnard Lawrence
June 2
nd
, 1999
(Priority Date)
US7017080B1 Method and system for determining a fault tree of a
technical system, computer program product and a
computer readable storage medium
Peter Liggesmeyer, Oliver
Maeckel,Michael Rettelbach,
Martin Rothfelder
Dec 6
th
, 1999
(Filing Date)
US6553360B1 Software-based problem-resolution production system
with standardized information providers & solution
interpreters
Matthew E. Hoekstra
Dec 14
th
, 2002
(Filing Date)
US6959263B2 Interactive diagnostic system and method driven by
expert system
Gerald A. Wilson, Aaron Wilson
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US Patent Search Results (phase 2)
The following search results represent the second continued search phase for “prior works” on the subject of
“Artificial Intelligence for Troubleshooting or Diagnosis”. This Prior art search (phase 2) was conducted in 2017 with
key words relating to the invention. The phase 2 search objective is more focused on Application troubleshooting and
related areas; and based on a different search source with minor differences in Key-phrases as specified below.
Source: - Google Patents.
Key-phrases/Criteria applied for Search (Phase 2):- For Patents granted or filed from 1975 until 2000 with
terms concerning “Artificial Intelligence” or “Expert System” meant for “troubleshooting” or “diagnosis”.
Above search results were further refined to exclude inventions for Medical or Technical component
diagnosis by title (Vehicle, Machine, Network, Electronic, Medical, and Genetic). Results data (3,270 results)
was further refined to a subset of inventions that – (i) apply automated diagnosis or troubleshooting for
Software or Application diagnosis or related areas (e.g. computer system) or (ii) involve “technical support”
(40 results) or (iii) involve “help desk” (36 results) or (iv) involve “application support” or “application
debugging” (1 result) or (v) involve CPC classifications for “error detection/correction & monitoring” (225
results) or “nonmedical diagnostics” (125 results). Note: - From each set of search results only certain patents
were selected to represent different sub-areas of application hence this is not a complete listing.
Table 5 List of US Patents Filed until year 2000-2002 (phase 2)
Date of Patent US Patent No. Patent Description Contributor(s)
Jun 16
th
, 1992 US5123017A Remote Maintenance Monitoring System Lorenz G. Simpkins, Richard C.
Owens, Donn A. Rochette
Aug 31
st
, 1993 US5241621A Management Issue Recognition and Resolution
Knowledge Processor
Ronald G. Smart
Aug 15
th
, 1995 US5442555A Combined expert system/neural networks method for
process fault diagnosis
Jaques Reifman, Thomas Y. C.
Wei
Sep 3
rd
, 1998
(Filed)
US6230287B1 Web Based Help Desk Debbie Pinard, et al
Aug 31
st
, 1999 US5944839A System and Method for Automatically Maintaining a
Computer System
Henri J. Isenberg
Sep 11
th
, 1999 US5983364A System and method for diagnosing computer faults Bortcosh et al
Nov 9
th
, 1999
(Filed)
US6371765B1 Interactive Computer-based Training System and
Method
Robert S. Wall, Donald R.
Warner, Jackie R. Closson
Jan 4
th
, 2000 US6012152A Software Fault Management System Samir Douik, Raouf Boutaba
Mar 31
st
, 2000
(Filed)
US6598179B1 Table-based Error Log Analysis Igor Chirashnya, Doron Erblich,
Raanan Gewirtesman
May 9
th
, 2000 US6061506A Adaptive Strategy-based System Graham Wollaston, Ray Farmer
May 10
th
, 2000
(Filed)
US6742141B1 System for Automated Problem Detection, Diagnosis,
and Resolution in a Software Driven System
Allan A. Miller
Sep 12
th
, 2000 US6118447A Apparatus and Methods for Analyzing Software
Systems
Avraham Harel
19. 15 | P a g e A I T r o u b l e s h o o t i n g I m p a c t A n a l y s i s
Summary of Search Results for “Artificial Intelligence” - USPTO (1976 to 2000)
A more general search for “Artificial Intelligence” was conducted (Patent Application dates from 1976 to 2000) –in
addition to the “AI for Troubleshooting” theme that this document is primarily concerned with. The generic search
revealed “AI” working in several interesting Tech areas that include- Genetic and Clinical Data Analysis (with diagnosis
and treatment recommendation for medical conditions) , Surgical path planning and Remote monitoring of patients
(with Q&A procedures); Smart Data Search Agents; for Interpretation and discovery of structures/patterns in data –
e.g. legal and pharmaceutical data; Digital Control Systems involving AI in various domains; AI controls to
autonomously adjust Device behavior working with Image recognition/ other sensors, AI for 3-D Object Recognition
(Neural Networks); AI for Natural Language Processing (NLP) parsers, item analysis for rework shop orders (Material
Requirements Planning), Intelligent Elevator Dispatching, noise reduction for speech processing, AI for optimized
computer processing, Network Management and selection of connectivity paths in a Communications network,
Telephone answering device with AI, Expert System for Automated Testing of Electronic units, AI for vehicle fault
diagnosis and testing (Vehicle Service Procedures).
PRIOR PUBLICATIONS
Publication Search Results (until 2000-2002)
The following chronological list of publications was formed based on the previous (patent) search keys. We consider
these representative prior works on the subject of Knowledge-based Diagnostic AI or Expert Systems as seen to be
applied for-- (a) traditional Technology-intensive domains (e.g. Medical, Turbine Generators, Chemical Plants) or (b)
relatively “new” areas of application (e.g. Decision Support, Process Modeling, Workflow) or (c) business-centric
domains (e.g. Real Estate, Finance, HRM). This is not a complete listing of publications on the subject.
Technical or Research Publications until 2000-2002
i. Artificial intelligence methods and systems for medical consultation: Kulikowski, Casimir A; IEEE Transactions
on pattern analysis and Machine Intelligence, Num. 5, 464-476, 1980; IEEE
ii. From Guidon to Neomycin and Heracles in Twenty Short Lessons: ORN Final Report 1979-1985; William J.
Clancy, Vol.7, Num.3, 40-60, 1986, The AI Magazine
iii. Knowledge-based report generation: A technique for automatically generating natural language reports from
databases: Kukich, Karen; ACM SIGIR Forum; Vol. 17, Num. 4; 246-250; 1983, ACM
iv. A Qualitative modeling shell for Process Diagnosis: T. F. Thompson, W. J. Clancy, IEEE Software, p. 6-15, 1986
v. On-line diagnosis of turbine-generators using artificial intelligence; Gonzalez, Avelino J; Osborne, Robert L;
Kemper, Chris T; Lowenfeld, Simon; IEEE transactions on energy conversion, Num. 2, 68-74, 1986, IEEE
vi. Automatic program debugging for intelligent tutoring systems: Murray, William R; Computational
Intelligence; Vol. 3, Num. 1, 1-16, 1987, Wiley Online Library
vii. Planning and Executing Office Procedures in Project ASPERA: Bena, M Cristina; Montini, Giorgio; Sirovich,
Franco; IJCAI; Vol. 87; 576-583; 1987
viii. Artificial intelligence in medical diagnosis: Szolovits, Peter; Patil, Ramesh S; Schwartz, William B; Annals of
internal medicine, Vol. 108, Num. 1, 80-87, 1988. Am Coll Physicians
ix. Understanding and debugging novice programs: Johnson, W Lewis; Artificial intelligence; Vol. 42, Num. 1, 51-
97, 1990, Elsevier
x. Artificial intelligence and the mass appraisal of residential apartments: Tay, Danny PH; Ho, David KH; Journal
of Property Valuation and Investment; Vol. 10, Num. 2, 525-540, 1992, MCB UP Ltd
20. 16 | P a g e A I T r o u b l e s h o o t i n g I m p a c t A n a l y s i s
xi. Strategic approaches to laboratory automation; McDowall, RD; Chemometrics and intelligent laboratory
systems; Vol 17, Num. 3, 259-264; 1992; Elsevier
xii. Biotech: A real-time application of artificial intelligence for fermentation processes: Steyer, J-Ph; Queinnec, I;
Simoes, Dr; Control Engineering Practice;Vol.1 Num.2; 315-321; 1993; Elsevier
xiii. Artificial intelligence in radiology: decision support systems: Kahn Jr, Charles E; Radiographics; Vol. 14, Num.
4, 849-861, 1994
xiv. Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian
experience): Altman, Edward I; Marco, Giancarlo; Varetto, Franco; Journal of banking & finance; Vol. 18, Num.
3, 505-529, 1994, Elsevier
xv. Artificial intelligence in HRM: an experimental study of an expert system: Lawler, John J; Elliot, Robin; Journal
of Management; Vol. 22, Num. 1, 85-111, 1996, Elsevier
xvi. MOBEDIC—A decision modelling tool for emergency situations: Doheny, JG; Fraser, JL; Expert Systems With
Applications; Vol. 10, Num. 1, 17-27; 1996; Elsevier
xvii. Survey of artificial intelligence methods for detection and identification of component faults in nuclear power
plants: Reifman, Jaques; Vol. 119, Num. 1, 76-97, 1997, Nuclear Technology
xviii. Help desk system with intelligent interface; Ho Kang, Byeong; Yoshida, Kenichi; Motoda, Hiroshi; Compton,
Paul; Applied Artificial Intelligence; Vol. 11, 611-631; 1997; Taylor & Francis
xix. An integrated case-based reasoning approach for intelligent help desk fault management; Law, Yuh Foong
David; Foong, Sew Bun; Kwan, Shee Eng Jeremiah; Expert Systems with Applications; Vol. 13, Num. 4, 265-
274, 1997, Elsevier
xx. A combined ANN and expert system tool for transformer fault diagnosis: Wang, Zhenyuan; Liu, Yilu; Griffin,
Paul J; IEEE Transactions on Power delivery; Vol. 13, Num. 4; 1224-1229; 1998; IEEE
xxi. Using Expert Systems and Artificial Intelligence for Real Estate Forecasting: Peter Rossini; School of
International Business, University of South Australia. Sixth Annual Pacific-Rim Real Estate Society Conference
Sydney, Australia, 24-27 January 2000
xxii. Process Modeling and AI Planning Techniques: A New Approach: MD R-Moreno, D. Borrajo, D. Meziat;
Departmento de Automatica, Universidad de Alcala (Madrid), Spain; Sep 26-28, 2000
xxiii. A knowledge-based approach to handling exceptions in workflow system: Klein, Mark; Dellarocas,
Chrysanthos; Computer Supported Cooperative Work (CSCW); Vol. 9, Num. 3, 399-412, 2000, Springer
xxiv. Neural network based framework for fault diagnosis in batch chemical plants: Ruiz, Diego; Nougués, José;
María; Calderón, Zuly; Espuña, Antonio; Puigjaner, Luis; Computers & Chemical Engineering; Vol 24; Num. 2-7;
777-784; 2000 ; Elsevier
xxv. An integrated help desk support for customer services over the World Wide Web—a case study; Foo,
Schubert; Hui, Siu Cheung; Leong, Peng Chor; Liu, Shigong; Computers in Industry; Vol. 41, Num. 2, 129-145;
2000; Elsevier
xxvi. AI planning and scheduling in the medical hospital environment: Spyropoulos, Constantine D; 2000; Elsevier
xxvii. Data mining for customer service support: Hui, Siu Cheung; Jha, G; Information & Management; Vol. 38, Num.
1, 1-13, 2000, North-Holland/Elsevier
21. 17 | P a g e A I T r o u b l e s h o o t i n g I m p a c t A n a l y s i s
Appendix
Figure 1 Online view of AI for Troubleshooting Plan/Paper published May 2009
http://bit.ly/AIPlan
After it was prepared in 2000 the “AI for Troubleshooting” B Plan/Proposal was submitted to Entrepreneur Networks.
From 2009 it was published online (same title) on a number of sites. Shown here is a site that is currently viewable.
Figure 2 Existing Yahoo Groups “SIFILES” (login required) Files with the “AI for Troubleshooting” B Plan (posted 2002)
Figure 3 Excerpt from Consultant S.Iyer’s E-Book
http://www.lulu.com/shop/shubhadha-iyer/ideas-in-people-management-e-book/ebook/product-23054877.html (Preview)
Excerpt from E-Book “Ideas in People Management”
on the application of “Intelligent Software Agent”
(ISA) technology in Management (See Preview)
23. 19 | P a g e A I T r o u b l e s h o o t i n g I m p a c t A n a l y s i s
PROOF OF THOUGHT LEADERSHIP ACTIVITY
Table 6 Proof of Activity
Volume of Work (With Links)
Business Plans/ Papers with original creation dates (2000-2002)
Yahoo Groups Files Folder: https://groups.yahoo.com/neo/groups/SIFiles/files
(First step, Login to yahoo.com with userid: sifiles1, passwd: group123 then access above link in browser)
Archived Email excerpts from S.Iyer (Google docs) subject: “AI software for Troubleshooting”: http://bit.ly/2k2zlp9
Title of work Originally
Prepared And
Promoted
Current URL Earliest Online
Publish Date
On the site
Online Format
Automated Troubleshooting,
Maintenance and Diagnostics for
Internal Technical Support
Sep 2000 (USA) http://bit.ly/AIPlan
http://bit.ly/AIPlan2
May 2009
Sep 2011
Business Plan
Business Plan
White Papers & Business Plans (online versions of previous original works by S.Iyer in 2000-2002 and 2011-2013)
White Papers by Shubhadha Iyer http://siyerconsult.com/white-papers
Business Plans and White Papers http://bit.ly/2hkaZEi
Career Portfolio https://Nexxt.com/p/ShubhadhaIyer
Online Presentations by Shubhadha Iyer (2013 - 2015)
Preview from “Ideas in People Management” (related to AI- ISA technology) http://bit.ly/2waFUsL
Shubhadha Iyer Management Career Bio http://bit.ly/SiyerBio
Management Consulting Experience https://youtu.be/-pMdVfGlhAk
PROOF OF ACTIVITY IN ARTIFICIAL INTELLIGENCE FOR TROUBLESHOOTING (2000) BY S.IYER DATE
1 Business Plan/ Proposal on “AI for Troubleshooting” was prepared by S.Iyer in USA
SEP 2000
2 Submitted by email to Silicon Valley Entrepreneur Networks, Forums and a few corporate authorities
promoting the Plan concept/ideas SEP 2000- 2002
3 Submitted (Email) abstract of the Plan concept to 123patent.com as a record of activity date/time
OCT 2000
4 Submitted the draft to a freelance professional to add a few paragraphs of Brochure-type material
OCT 2000
5 Included for participation at the “Inv-Ent” Forum in USA
DEC 2000
6 Yahoo groups Files folder shows the “AI for Troubleshooting” Plan uploaded with date/time.
NOV 2002
7 Gave an official Plan presentation to Indo-US VC Firm in Bangalore, India
JUN 2007
8 Plan currently viewable online – book publishing site with full preview and published date
MAY 2009-2011
