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© 2017 IBM Corporation
Hamid R. Motahari-Nezhad
IBM Almaden Research Center
San Jose, CA
From Services to Cogs and the Journey to Cognitive
BPM
SOSE 2017 – San Francisco
09 March 2017
© 2013 IBM Corporation© 2017 IBM Corporation
COGNITIVE
The Future of Computing is …..
2
© 2013 IBM Corporation© 2017 IBM Corporation
Cognitive is emerging as a new computing paradigm
Tabulating
Systems Era
Programmable Systems Era
Cognitive
Systems Era
© 2013 IBM Corporation© 2017 IBM Corporation
Cognitive Era
4
Discovery & Recommendation
Probabilistic
Big (Dark) Data
Natural Language as the Interface
Intelligent Options
© 2013 IBM Corporation© 2017 IBM Corporation
Understands
natural language
and human
communication
Adapts and learns
from user
selections and
responses
Generates and
evaluates
evidence-based
hypothesis
Cognitive System
1
2
3 Cognitive Systems do actively
discover, learn and act
A Cognitive System offers computational capabilities typically based on Natural Language Processing (NLP),
Machine Learning (ML), and reasoning chains, on large amount of data, which provides cognition powers that
augment and scale human expertise
Watson
© 2013 IBM Corporation© 2017 IBM Corporation
Towards Computing-At-Scale as the Shared Characteristic of Recent Advances
6
Scalable Computing over
MassiveCommodity Hardware
Building Stronger
Super Computers
Cloud Computing
Crowd Computing
Advanced individual
algorithms
Mass computing applied to AI Complex array of algorithms applied to make
sense of data, and offer cognitive assistance
Big
Data
Individual
MLAlgorithm
Cognitive Computing
© 2013 IBM Corporation© 2017 IBM Corporation
The Future of Work is Cognitive
7
The Evolution of Collaboration Technology In the Enterprise
The Rise of
Intelligent
Personal
Assistant
© 2013 IBM Corporation© 2017 IBM Corporation
Intelligent Assistance and Related Technology – App Landscape
8
IPSoft’s
Amelia
© 2013 IBM Corporation© 2017 IBM Corporation
We have seen just the tip of the iceberg…
9
Gartner Technology
Hype Cycle - 2015
© 2013 IBM Corporation© 2017 IBM Corporation
How service computing continues to transform software
engineering, and how service computing needs to evolve?
The Future of Services is Cognitive…!
10
© 2013 IBM Corporation© 2017 IBM Corporation
UX / Front OfficeBackend / Back Office
Service Orientation: Transformation from Backend to Front-End
11
90’s 2000’s 2010’s 2020’s
• RPC
• CORBA
• RMI
• DCOM
• XML-
RPC
• SOAP
• WSDL
• REST
• JSON
• Microservice
architecture
• APIService
Monolithic
Application
BPEL
BPMN
Mashup
Composition
EDI/
EDIFACT
Knowledge Graph
Data
Integration
Conversational
API
Cognitive Composition
Cognitive BPM
Natural Language
© 2013 IBM Corporation© 2017 IBM Corporation
The Evolution of Human-Computer Interface
1984 2007
§ For so long, the default UX paradigm has been graphical
– From Desktops to Mobile
12
Communication
Apps
Push
Notifications
§ The exponential growth of apps on mobile created problems
– Users do not want to go to individual apps, app growth stalled
– Communication apps (messaging) became the most used apps by users,
both in private life and in the enterprise
– Notification mechanism is widespread, but addressing only App-to-Human
interaction
• Significantly has reduced the app visits – some called a phenomenon
that means “the end of Apps as we know it”
Bot Platform Slack
IBM
Microsoft
§ Bots/Cogs are becoming the new application-human interaction
mechanism capable of handling bi-directional interactions
– Bots are speaking human language
– Bots expected to understand human language and interact with our APIs
• Natural Language Understanding has come a long way
• Many messaging apps and platforms have made bots as first class
offerings
© 2013 IBM Corporation© 2017 IBM Corporation
From Programmatic APIs to Conversational APIs
How the API design should be transformed?
13
Smart/ Conversational
API
{
”API": ”calendar.create",
”Invitee": [”John Smith"],
”Time": "03-09-2017"
}
NL-API Interface
Service
The NL Interface gives the API the intent, and the entities. Conversational API works with minimal parameters.
Dialog
Schedule a meeting with John Smith for tomorrow There are two John Smiths in your contacts.
I suppose you mean …
The only open time that you have tomorrow
is between 12-1pm, do you want to make it
a lunch meeting?
Can I work with John’s assistant to find a
time…?
Bot-to-Bot communication language
API (REST)/
JSON
NL-API Interface
Service
{
”API": ”calendar.create",
”Subject": ”Meeting with John Smith",
”Required": [”john.smith@address"],
”Time": "03-09-2017 12:00:00+08:00"
}
Schedule a meeting with John Smith for Noon tomorrow
The NL Interface (Bot) creates the API call – Non-Conversational API
Confirmation
or Failure
• Cog sends error if all expected
parameters are not provided.
• Cog has no application insight.
• It may run a generic dialog to
get all information
© 2013 IBM Corporation© 2017 IBM Corporation
The transformation of Service/People Composition
§ In current Hybrid composition/mashup (People,
Services) methods:
– Services are represented with API calls
– People are integrated with Human Tasks (GUI
is the interaction paradigm)
– Composition methods are finding deterministic
models of interactions, defined apriori
§ We are moving towards dynamic composition of
cogs and human in which
– Cogs are participating in NL conversations
– Human are approached through messaging
and natural language
– Composition are performed dynamically during
the conversation,require non-deterministic
models, defined in online and on-demand
model
14
Weather
Cog
Health
Agent
Personality
Insight Cog.
Provider
Cogs
Travel Cog 1
Travel Cog 2
Planning a Vacation
Trip
Considering preferences,
experience, conditions, cost,
Availability, etc.
Mediated and facilitated by Cogs
Human-Cog interaction
Cog-Cog interaction
Natural Language
Natural Language, CCL,
(ACL, KQML, etc.)?
ACL: Agent Communication Language, KQML, etc.
© 2013 IBM Corporation© 2017 IBM Corporation
The App Composition (Mashup) is already moving away from explicit API calls at All Times
§ Implicit Data Sharing with the notion of Central Shared
Context on Mobile Platforms
– Events
– Notifications
– Metadata descriptions
§ Google Now on Tap (implicit integration)
– Central Shared Context
§ Apple Proactive Assistant
– Proactive recommendations and suggestions across
apps
15
© 2013 IBM Corporation© 2017 IBM Corporation
THE JOURNEY TO COGNITIVE BPM
How advances in Cognitive Computing (AI and Machine Learning) impacts BPM?
16
© 2013 IBM Corporation© 2017 IBM Corporation
A significant portion of processes are yet to be automated!
§ Today’s process automation is centered on high-
volume, routine processes
§ According to some estimates, the low-volume
processes cost twice as those of routine
processes in the same industry
Long Tail of
Low-Volume Processes
Volume
… …
Spectrum of business processes
Low volume, but potentially high
cost, and high value processes
• Monthly
Payroll Run
• Employee Income
Verification
• Personal Insurance
Underwriting
• Validating Inputs for
Payroll Run & Approvals
• Income of Self-Employed
Borrowers
• Commercial Insurance
Underwriting
§ Cognitive Computing can bring the ability to
automate, optimize & transform “long tail”
processes
© 2013 IBM Corporation© 2017 IBM Corporation
Knowledge
Worker
Work Management Software: Long Tail Support
18
As most long tail processes have defied automation,
purpose-build collaboration applications have served the need!
Robotic Process Automation (RPA)
Automating routine, repetitive, yet unstructured
steps that are done manually
$2.5B Market in 2017, estimated $8B by 2024
© 2013 IBM Corporation© 2017 IBM Corporation
Historical and Future Perspectives on BPM
19
Databases
BackendSystems
Layer
Self-Generating Integration
SAP using
java
API
Web
Service
API
Excel using
com
API
MSMQ using
com or java
API
Databases using
jdbc
API
Business
Rules
Layer
Production
Business Level
Objects
Business Level Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
General Workflow System and UserInteractionsCalculation
Interface
Layer
Web
Service
Presentation Presentation
XML
API
BackendSystems
Layer
Self-Generating Integration
SAP using
java
API
SAP using
java
API
Web
Service
API
Web
Service
API
Excel using
com
API
Excel using
com
API
MSMQ using
com or java
API
MSMQ using
com or java
API
Databases using
jdbc
API
Databases using
jdbc
API
Business
Rules
Layer
Production
Business Level
Objects
Business Level Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
General Workflow System and UserInteractionsCalculation
Interface
Layer
Web
Service
PresentationPresentation PresentationPresentation
XML
API
XML
API
BPMS
TQM
General Workflow
BPR
BPM
time
ERP
WFM
EAI
‘85 ‘90 ‘95 ‘05‘00‘98
IT Innovations
Management Concepts
DatabasesDatabases
BackendSystems
Layer
Self-Generating Integration
SAP using
java
API
Web
Service
API
Excel using
com
API
MSMQ using
com or java
API
Databases using
jdbc
API
Business
Rules
Layer
Production
Business Level
Objects
Business Level Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
General Workflow System and UserInteractionsCalculation
Interface
Layer
Web
Service
Presentation Presentation
XML
API
BackendSystems
Layer
Self-Generating Integration
SAP using
java
API
SAP using
java
API
Web
Service
API
Web
Service
API
Excel using
com
API
Excel using
com
API
MSMQ using
com or java
API
MSMQ using
com or java
API
Databases using
jdbc
API
Databases using
jdbc
API
Business
Rules
Layer
Production
Business Level
Objects
Business Level Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
General Workflow System and UserInteractionsCalculation
Interface
Layer
Web
Service
PresentationPresentation PresentationPresentation
XML
API
XML
API
BPMS
BackendSystems
Layer
Self-Generating Integration
SAP using
java
API
Web
Service
API
Excel using
com
API
MSMQ using
com or java
API
Databases using
jdbc
API
Business
Rules
Layer
Production
Business Level
Objects
Business Level Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
General Workflow System and UserInteractionsCalculation
Interface
Layer
Web
Service
Presentation Presentation
XML
API
BackendSystems
Layer
Self-Generating Integration
SAP using
java
API
SAP using
java
API
Web
Service
API
Web
Service
API
Excel using
com
API
Excel using
com
API
MSMQ using
com or java
API
MSMQ using
com or java
API
Databases using
jdbc
API
Databases using
jdbc
API
Business
Rules
Layer
Production
Business Level
Objects
Business Level Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
General Workflow System and UserInteractionsCalculation
Interface
Layer
Web
Service
PresentationPresentation PresentationPresentation
XML
API
XML
API
BPMS
TQMTQM
General Workflow
BPRGeneral Workflow
BPR
BPMBPMBPM
time
ERPERP
WFMWFM
EAIEAI
‘85 ‘90 ‘95 ‘05‘00‘98
IT Innovations
Management Concepts
Ref: Ravesteyn, 2007
‘15
Social BPM
iBPMS: Business
Process Analytics
‘2020
The Future of BPM is also Cognitive
Dark Data
Cognitive BPM
Cognitive
Analytics
Cognitive
Processes
Act
LearnPlan
Cognitive
Capabilities
© 2013 IBM Corporation© 2017 IBM Corporation
Dark Data: digital footprint of people, systems, apps and IoT devices
§ Handling and managing work (processes) involves interaction among employees, systems and devices
§ Interactions are happing over email, chat, messaging apps, and
§ There are descriptions of processes, procedures, policies, laws, rules, regulations, plans, external entities such as
customers, partners and government agenies, surrounding world, news, social networks, etc.
§ The need for activities over interactions of people, systems, and IoT devices to be coordinate
20
Citizens
Assistant
Business
Employees/
agents
Plans
Rules
Policies
Regulations
TemplatesInstructions/
Procedures
ApplicationsSchedules
Communications such as
email, chat, social media,
etc.
Organization
Dark Data: Unstructured Linked Information
IoT Devices and Sensors
© 2013 IBM Corporation© 2017 IBM Corporation
Spectrum of Work: Processes and Cognitive
21
Structured Processes
Unstructured Processes
Knowledge-based
Routine
Existing Technology
Dark Data: Mobile, Social, Communication (email, voice, video), Documents, Notes
IoT, Smart Devices
BPM
Engines
Workflow
Engines
Case
Management
Groupware
Knowledge-Intensive
Processes
Email, Chat, Messaging
Ad-hoc, unstructured
Processes
Cognitive in Process Management
Cognitive
Interface for
Process Engines
Cognitive Process
Discovery and
Learning
Cognitive
Process
Analytics
Cognitive Process
Automation and
Enablement
© 2013 IBM Corporation© 2017 IBM Corporation
Cognitive BPM Lifecycle
22
Cognitive
BPMS
Define
Enact
Monitor
Analyze
Next Steps, Adapt
Interact
Sense
Learn,
Discover
To
Traditional BPM Cognitive BPM
© 2013 IBM Corporation© 2017 IBM Corporation
Cognitively-Enabled Processes: Shifting process lifecycle
from Define-Execute-Analyze-Improve to Plan-Act-Learn
§ It’s about enabling the building of situational processes on the
fly!
§ For each enactment of the overall process, many iterations around
the Plan, Act, Learn loop
§ At a given time, multiple goals & sub-goals may be active
§ As new information arrives the cycle might re-start for some or all
threads
– Planning based on new info
• New goal formulation
• Planning to achieve those goals
– Act on next steps of plan
– Optionally perform Learning steps
§ Cognitive agents (Cogs) helps by
– Perform the planning
– Learn from large volumes of structured/unstructured data
– Over time, learn best practices and incorporate into planning
– Deliver a conversational interface in the course of process
execution to the user
Plan /
Decide
Act
<<World Effect>>
Learn
Rick Hull,Hamid R. Motahari Nezhad:Rethinking BPM in a Cognitive World:
Transforming How We Learn and Perform Business Processes.BPM 2016:3-19.
© 2013 IBM Corporation© 2017 IBM Corporation
Cognitive BPM Systems
§ A Cognitive BPM system is a cognitive system that provides cognitive support in all phases of a
process lifecycle over structured and unstructured information sources, and is able to
continuously discover, learn and proactively act to support achieving a desired outcome
– It offers cognitive interaction and analytics support over structured processes
– For unstructured processes, it offers intelligent and integrated process (model) definition,
reasoning and adaptation
• Process is not assumed apriori defined; but is discovered, learned and customized based
on accumulated knowledge and experience
– It continually learns to improve the process
• Optimizing the process
• Proactive recommendation in the planning phase & the next step to act
24
© 2013 IBM Corporation© 2017 IBM Corporation
eAssistant: Extracting process steps from human conversations
§ A cognitive assistant to extract process steps from emails and proactively advise knowledge workers
§ Extracts actionable statements, commitments, temporal aspects from conversation texr (e.g., emails)
§ Learns through user feedback,reinforcement learning, and customization to org’s and people
Hamid R. Motahari Nezhad, K. Gunaratna, J. Cappi, eAssistant: Cognitive Assistance for Identification and Auto-Triage of Actionable Conversations,
WWW’2017.
© 2013 IBM Corporation© 2017 IBM Corporation
Learning of Actionable Statements Identification § Bootstrapping
– Start with rules-
and annotation-
based training data
generation
– Learn from user
feedback
§ Validation
– >6K emails from
Enron data set
– >15.5K candidate
actionable
mentions
– Hand-annotated
for ground truth
– After training,
Precision: 89%
Recall: 86%
§ Note: User feedback
enables continued
improvements
Model	Learning	– Training	
Phase:	Classified	Statements
Action Verb
Learning
Adaptive	&	
Online	Pattern	
Learning
Actionable	
Statement
Identification	
Results	+	Type
Pattern-Based
Action
Prediction
is	action	verb
yes no
No
Pattern
Construction
Filtering
Feedback
Prediction:	Statement
Action
verbs
Ontology
Feature Extraction
POS,	NLP	Tags,	Verbs	And	
Dependency	Extraction
Feature Extraction
POS,	NLP	Tags,	Verbs	And	
Dependency	Extraction
Model	
<Adaptive	Patterns>
verbs All	features All	features
is verb tagged
as actionable?
yes no
enclosed verb
yes no
verb can be
independent
verb is
dependent
Verb Independent ?
send true
prepare true
like false
have false
- - - - - -
© 2013 IBM Corporation© 2017 IBM Corporation
27
Inbox - Verse Highlighting actionable statements Recommending fulfilment actions
IBM Insight 2015 – The session on “Given your collaboration tools a brain”
© 2013 IBM Corporation© 2017 IBM Corporation
28
IBM Insight 2015 – The session on “Given your collaboration tools a brain”
Invite/Calendar Action Archetype Automated Invite Parameters Extraction Calendar Entry Creation
© 2013 IBM Corporation© 2017 IBM Corporation
Cognitive BPM: Research Directions
§ Abstractions and models for Cognitive Processes
§ Cognitive process learning: knowledge acquisition methods from unstructured information from
dark data, IoT and smart devices
§ Cognitive Work Assistants
–Cognitive augmentation of workers in work environments, and in process management
§ Cognitive Process Management System
–Analytics on unstructured information to support process understanding
–Analytics to support process adaptation, customization and configuration
–Proactive process adaptation
§ Learning and teaching tasks and processes to cognitive agents
–Interactive learning where cognitive agents ask process questions
–Gradual learning through experience, and process improvement
§ Applications of deep learning for cognitive process learning, enablement and automation
29
© 2013 IBM Corporation© 2017 IBM Corporation
Summary
§ The Future of Computing is ….
§ The Future of Work is ….
§ The Future of Services is ….
§ The Future of UX is ….
§ The Future of BPM is ….
30
Cognitive
Cognitive Computing
Cognitive Work Assistants
Cognitive Services (Conversational APIs)
Cognitive Agents (Cogs/Bots)
Cognitive BPM
An unprecedented opportunity for the services research community to advance our
understanding, methods and technology underpinning these transformations and disruptions!
© 2013 IBM Corporation© 2017 IBM Corporation
31
https://sites.google.com/site/cbpm2017/
© 2013 IBM Corporation© 2017 IBM Corporation
QUESTIONS?
Thank You!
32
Hamid R. Motahari Nezhad
IBM Almaden Research Center
Contact: motahari@us.ibm.com
Website: http://hamidmotahari.info
Twitter: @hamidmotahari

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From Services to Cogs and Journey to Cognitive BPM

  • 1. © 2017 IBM Corporation Hamid R. Motahari-Nezhad IBM Almaden Research Center San Jose, CA From Services to Cogs and the Journey to Cognitive BPM SOSE 2017 – San Francisco 09 March 2017
  • 2. © 2013 IBM Corporation© 2017 IBM Corporation COGNITIVE The Future of Computing is ….. 2
  • 3. © 2013 IBM Corporation© 2017 IBM Corporation Cognitive is emerging as a new computing paradigm Tabulating Systems Era Programmable Systems Era Cognitive Systems Era
  • 4. © 2013 IBM Corporation© 2017 IBM Corporation Cognitive Era 4 Discovery & Recommendation Probabilistic Big (Dark) Data Natural Language as the Interface Intelligent Options
  • 5. © 2013 IBM Corporation© 2017 IBM Corporation Understands natural language and human communication Adapts and learns from user selections and responses Generates and evaluates evidence-based hypothesis Cognitive System 1 2 3 Cognitive Systems do actively discover, learn and act A Cognitive System offers computational capabilities typically based on Natural Language Processing (NLP), Machine Learning (ML), and reasoning chains, on large amount of data, which provides cognition powers that augment and scale human expertise Watson
  • 6. © 2013 IBM Corporation© 2017 IBM Corporation Towards Computing-At-Scale as the Shared Characteristic of Recent Advances 6 Scalable Computing over MassiveCommodity Hardware Building Stronger Super Computers Cloud Computing Crowd Computing Advanced individual algorithms Mass computing applied to AI Complex array of algorithms applied to make sense of data, and offer cognitive assistance Big Data Individual MLAlgorithm Cognitive Computing
  • 7. © 2013 IBM Corporation© 2017 IBM Corporation The Future of Work is Cognitive 7 The Evolution of Collaboration Technology In the Enterprise The Rise of Intelligent Personal Assistant
  • 8. © 2013 IBM Corporation© 2017 IBM Corporation Intelligent Assistance and Related Technology – App Landscape 8 IPSoft’s Amelia
  • 9. © 2013 IBM Corporation© 2017 IBM Corporation We have seen just the tip of the iceberg… 9 Gartner Technology Hype Cycle - 2015
  • 10. © 2013 IBM Corporation© 2017 IBM Corporation How service computing continues to transform software engineering, and how service computing needs to evolve? The Future of Services is Cognitive…! 10
  • 11. © 2013 IBM Corporation© 2017 IBM Corporation UX / Front OfficeBackend / Back Office Service Orientation: Transformation from Backend to Front-End 11 90’s 2000’s 2010’s 2020’s • RPC • CORBA • RMI • DCOM • XML- RPC • SOAP • WSDL • REST • JSON • Microservice architecture • APIService Monolithic Application BPEL BPMN Mashup Composition EDI/ EDIFACT Knowledge Graph Data Integration Conversational API Cognitive Composition Cognitive BPM Natural Language
  • 12. © 2013 IBM Corporation© 2017 IBM Corporation The Evolution of Human-Computer Interface 1984 2007 § For so long, the default UX paradigm has been graphical – From Desktops to Mobile 12 Communication Apps Push Notifications § The exponential growth of apps on mobile created problems – Users do not want to go to individual apps, app growth stalled – Communication apps (messaging) became the most used apps by users, both in private life and in the enterprise – Notification mechanism is widespread, but addressing only App-to-Human interaction • Significantly has reduced the app visits – some called a phenomenon that means “the end of Apps as we know it” Bot Platform Slack IBM Microsoft § Bots/Cogs are becoming the new application-human interaction mechanism capable of handling bi-directional interactions – Bots are speaking human language – Bots expected to understand human language and interact with our APIs • Natural Language Understanding has come a long way • Many messaging apps and platforms have made bots as first class offerings
  • 13. © 2013 IBM Corporation© 2017 IBM Corporation From Programmatic APIs to Conversational APIs How the API design should be transformed? 13 Smart/ Conversational API { ”API": ”calendar.create", ”Invitee": [”John Smith"], ”Time": "03-09-2017" } NL-API Interface Service The NL Interface gives the API the intent, and the entities. Conversational API works with minimal parameters. Dialog Schedule a meeting with John Smith for tomorrow There are two John Smiths in your contacts. I suppose you mean … The only open time that you have tomorrow is between 12-1pm, do you want to make it a lunch meeting? Can I work with John’s assistant to find a time…? Bot-to-Bot communication language API (REST)/ JSON NL-API Interface Service { ”API": ”calendar.create", ”Subject": ”Meeting with John Smith", ”Required": [”john.smith@address"], ”Time": "03-09-2017 12:00:00+08:00" } Schedule a meeting with John Smith for Noon tomorrow The NL Interface (Bot) creates the API call – Non-Conversational API Confirmation or Failure • Cog sends error if all expected parameters are not provided. • Cog has no application insight. • It may run a generic dialog to get all information
  • 14. © 2013 IBM Corporation© 2017 IBM Corporation The transformation of Service/People Composition § In current Hybrid composition/mashup (People, Services) methods: – Services are represented with API calls – People are integrated with Human Tasks (GUI is the interaction paradigm) – Composition methods are finding deterministic models of interactions, defined apriori § We are moving towards dynamic composition of cogs and human in which – Cogs are participating in NL conversations – Human are approached through messaging and natural language – Composition are performed dynamically during the conversation,require non-deterministic models, defined in online and on-demand model 14 Weather Cog Health Agent Personality Insight Cog. Provider Cogs Travel Cog 1 Travel Cog 2 Planning a Vacation Trip Considering preferences, experience, conditions, cost, Availability, etc. Mediated and facilitated by Cogs Human-Cog interaction Cog-Cog interaction Natural Language Natural Language, CCL, (ACL, KQML, etc.)? ACL: Agent Communication Language, KQML, etc.
  • 15. © 2013 IBM Corporation© 2017 IBM Corporation The App Composition (Mashup) is already moving away from explicit API calls at All Times § Implicit Data Sharing with the notion of Central Shared Context on Mobile Platforms – Events – Notifications – Metadata descriptions § Google Now on Tap (implicit integration) – Central Shared Context § Apple Proactive Assistant – Proactive recommendations and suggestions across apps 15
  • 16. © 2013 IBM Corporation© 2017 IBM Corporation THE JOURNEY TO COGNITIVE BPM How advances in Cognitive Computing (AI and Machine Learning) impacts BPM? 16
  • 17. © 2013 IBM Corporation© 2017 IBM Corporation A significant portion of processes are yet to be automated! § Today’s process automation is centered on high- volume, routine processes § According to some estimates, the low-volume processes cost twice as those of routine processes in the same industry Long Tail of Low-Volume Processes Volume … … Spectrum of business processes Low volume, but potentially high cost, and high value processes • Monthly Payroll Run • Employee Income Verification • Personal Insurance Underwriting • Validating Inputs for Payroll Run & Approvals • Income of Self-Employed Borrowers • Commercial Insurance Underwriting § Cognitive Computing can bring the ability to automate, optimize & transform “long tail” processes
  • 18. © 2013 IBM Corporation© 2017 IBM Corporation Knowledge Worker Work Management Software: Long Tail Support 18 As most long tail processes have defied automation, purpose-build collaboration applications have served the need! Robotic Process Automation (RPA) Automating routine, repetitive, yet unstructured steps that are done manually $2.5B Market in 2017, estimated $8B by 2024
  • 19. © 2013 IBM Corporation© 2017 IBM Corporation Historical and Future Perspectives on BPM 19 Databases BackendSystems Layer Self-Generating Integration SAP using java API Web Service API Excel using com API MSMQ using com or java API Databases using jdbc API Business Rules Layer Production Business Level Objects Business Level Objects Inv oices Business Lev el Obj ects AFE’s Business Level Objects Anything Business Level Objects Process Layer Any Process General Workflow System and UserInteractionsCalculation Interface Layer Web Service Presentation Presentation XML API BackendSystems Layer Self-Generating Integration SAP using java API SAP using java API Web Service API Web Service API Excel using com API Excel using com API MSMQ using com or java API MSMQ using com or java API Databases using jdbc API Databases using jdbc API Business Rules Layer Production Business Level Objects Business Level Objects Inv oices Business Lev el Obj ects AFE’s Business Level Objects Anything Business Level Objects Process Layer Any Process General Workflow System and UserInteractionsCalculation Interface Layer Web Service PresentationPresentation PresentationPresentation XML API XML API BPMS TQM General Workflow BPR BPM time ERP WFM EAI ‘85 ‘90 ‘95 ‘05‘00‘98 IT Innovations Management Concepts DatabasesDatabases BackendSystems Layer Self-Generating Integration SAP using java API Web Service API Excel using com API MSMQ using com or java API Databases using jdbc API Business Rules Layer Production Business Level Objects Business Level Objects Inv oices Business Lev el Obj ects AFE’s Business Level Objects Anything Business Level Objects Process Layer Any Process General Workflow System and UserInteractionsCalculation Interface Layer Web Service Presentation Presentation XML API BackendSystems Layer Self-Generating Integration SAP using java API SAP using java API Web Service API Web Service API Excel using com API Excel using com API MSMQ using com or java API MSMQ using com or java API Databases using jdbc API Databases using jdbc API Business Rules Layer Production Business Level Objects Business Level Objects Inv oices Business Lev el Obj ects AFE’s Business Level Objects Anything Business Level Objects Process Layer Any Process General Workflow System and UserInteractionsCalculation Interface Layer Web Service PresentationPresentation PresentationPresentation XML API XML API BPMS BackendSystems Layer Self-Generating Integration SAP using java API Web Service API Excel using com API MSMQ using com or java API Databases using jdbc API Business Rules Layer Production Business Level Objects Business Level Objects Inv oices Business Lev el Obj ects AFE’s Business Level Objects Anything Business Level Objects Process Layer Any Process General Workflow System and UserInteractionsCalculation Interface Layer Web Service Presentation Presentation XML API BackendSystems Layer Self-Generating Integration SAP using java API SAP using java API Web Service API Web Service API Excel using com API Excel using com API MSMQ using com or java API MSMQ using com or java API Databases using jdbc API Databases using jdbc API Business Rules Layer Production Business Level Objects Business Level Objects Inv oices Business Lev el Obj ects AFE’s Business Level Objects Anything Business Level Objects Process Layer Any Process General Workflow System and UserInteractionsCalculation Interface Layer Web Service PresentationPresentation PresentationPresentation XML API XML API BPMS TQMTQM General Workflow BPRGeneral Workflow BPR BPMBPMBPM time ERPERP WFMWFM EAIEAI ‘85 ‘90 ‘95 ‘05‘00‘98 IT Innovations Management Concepts Ref: Ravesteyn, 2007 ‘15 Social BPM iBPMS: Business Process Analytics ‘2020 The Future of BPM is also Cognitive Dark Data Cognitive BPM Cognitive Analytics Cognitive Processes Act LearnPlan Cognitive Capabilities
  • 20. © 2013 IBM Corporation© 2017 IBM Corporation Dark Data: digital footprint of people, systems, apps and IoT devices § Handling and managing work (processes) involves interaction among employees, systems and devices § Interactions are happing over email, chat, messaging apps, and § There are descriptions of processes, procedures, policies, laws, rules, regulations, plans, external entities such as customers, partners and government agenies, surrounding world, news, social networks, etc. § The need for activities over interactions of people, systems, and IoT devices to be coordinate 20 Citizens Assistant Business Employees/ agents Plans Rules Policies Regulations TemplatesInstructions/ Procedures ApplicationsSchedules Communications such as email, chat, social media, etc. Organization Dark Data: Unstructured Linked Information IoT Devices and Sensors
  • 21. © 2013 IBM Corporation© 2017 IBM Corporation Spectrum of Work: Processes and Cognitive 21 Structured Processes Unstructured Processes Knowledge-based Routine Existing Technology Dark Data: Mobile, Social, Communication (email, voice, video), Documents, Notes IoT, Smart Devices BPM Engines Workflow Engines Case Management Groupware Knowledge-Intensive Processes Email, Chat, Messaging Ad-hoc, unstructured Processes Cognitive in Process Management Cognitive Interface for Process Engines Cognitive Process Discovery and Learning Cognitive Process Analytics Cognitive Process Automation and Enablement
  • 22. © 2013 IBM Corporation© 2017 IBM Corporation Cognitive BPM Lifecycle 22 Cognitive BPMS Define Enact Monitor Analyze Next Steps, Adapt Interact Sense Learn, Discover To Traditional BPM Cognitive BPM
  • 23. © 2013 IBM Corporation© 2017 IBM Corporation Cognitively-Enabled Processes: Shifting process lifecycle from Define-Execute-Analyze-Improve to Plan-Act-Learn § It’s about enabling the building of situational processes on the fly! § For each enactment of the overall process, many iterations around the Plan, Act, Learn loop § At a given time, multiple goals & sub-goals may be active § As new information arrives the cycle might re-start for some or all threads – Planning based on new info • New goal formulation • Planning to achieve those goals – Act on next steps of plan – Optionally perform Learning steps § Cognitive agents (Cogs) helps by – Perform the planning – Learn from large volumes of structured/unstructured data – Over time, learn best practices and incorporate into planning – Deliver a conversational interface in the course of process execution to the user Plan / Decide Act <<World Effect>> Learn Rick Hull,Hamid R. Motahari Nezhad:Rethinking BPM in a Cognitive World: Transforming How We Learn and Perform Business Processes.BPM 2016:3-19.
  • 24. © 2013 IBM Corporation© 2017 IBM Corporation Cognitive BPM Systems § A Cognitive BPM system is a cognitive system that provides cognitive support in all phases of a process lifecycle over structured and unstructured information sources, and is able to continuously discover, learn and proactively act to support achieving a desired outcome – It offers cognitive interaction and analytics support over structured processes – For unstructured processes, it offers intelligent and integrated process (model) definition, reasoning and adaptation • Process is not assumed apriori defined; but is discovered, learned and customized based on accumulated knowledge and experience – It continually learns to improve the process • Optimizing the process • Proactive recommendation in the planning phase & the next step to act 24
  • 25. © 2013 IBM Corporation© 2017 IBM Corporation eAssistant: Extracting process steps from human conversations § A cognitive assistant to extract process steps from emails and proactively advise knowledge workers § Extracts actionable statements, commitments, temporal aspects from conversation texr (e.g., emails) § Learns through user feedback,reinforcement learning, and customization to org’s and people Hamid R. Motahari Nezhad, K. Gunaratna, J. Cappi, eAssistant: Cognitive Assistance for Identification and Auto-Triage of Actionable Conversations, WWW’2017.
  • 26. © 2013 IBM Corporation© 2017 IBM Corporation Learning of Actionable Statements Identification § Bootstrapping – Start with rules- and annotation- based training data generation – Learn from user feedback § Validation – >6K emails from Enron data set – >15.5K candidate actionable mentions – Hand-annotated for ground truth – After training, Precision: 89% Recall: 86% § Note: User feedback enables continued improvements Model Learning – Training Phase: Classified Statements Action Verb Learning Adaptive & Online Pattern Learning Actionable Statement Identification Results + Type Pattern-Based Action Prediction is action verb yes no No Pattern Construction Filtering Feedback Prediction: Statement Action verbs Ontology Feature Extraction POS, NLP Tags, Verbs And Dependency Extraction Feature Extraction POS, NLP Tags, Verbs And Dependency Extraction Model <Adaptive Patterns> verbs All features All features is verb tagged as actionable? yes no enclosed verb yes no verb can be independent verb is dependent Verb Independent ? send true prepare true like false have false - - - - - -
  • 27. © 2013 IBM Corporation© 2017 IBM Corporation 27 Inbox - Verse Highlighting actionable statements Recommending fulfilment actions IBM Insight 2015 – The session on “Given your collaboration tools a brain”
  • 28. © 2013 IBM Corporation© 2017 IBM Corporation 28 IBM Insight 2015 – The session on “Given your collaboration tools a brain” Invite/Calendar Action Archetype Automated Invite Parameters Extraction Calendar Entry Creation
  • 29. © 2013 IBM Corporation© 2017 IBM Corporation Cognitive BPM: Research Directions § Abstractions and models for Cognitive Processes § Cognitive process learning: knowledge acquisition methods from unstructured information from dark data, IoT and smart devices § Cognitive Work Assistants –Cognitive augmentation of workers in work environments, and in process management § Cognitive Process Management System –Analytics on unstructured information to support process understanding –Analytics to support process adaptation, customization and configuration –Proactive process adaptation § Learning and teaching tasks and processes to cognitive agents –Interactive learning where cognitive agents ask process questions –Gradual learning through experience, and process improvement § Applications of deep learning for cognitive process learning, enablement and automation 29
  • 30. © 2013 IBM Corporation© 2017 IBM Corporation Summary § The Future of Computing is …. § The Future of Work is …. § The Future of Services is …. § The Future of UX is …. § The Future of BPM is …. 30 Cognitive Cognitive Computing Cognitive Work Assistants Cognitive Services (Conversational APIs) Cognitive Agents (Cogs/Bots) Cognitive BPM An unprecedented opportunity for the services research community to advance our understanding, methods and technology underpinning these transformations and disruptions!
  • 31. © 2013 IBM Corporation© 2017 IBM Corporation 31 https://sites.google.com/site/cbpm2017/
  • 32. © 2013 IBM Corporation© 2017 IBM Corporation QUESTIONS? Thank You! 32 Hamid R. Motahari Nezhad IBM Almaden Research Center Contact: motahari@us.ibm.com Website: http://hamidmotahari.info Twitter: @hamidmotahari