IBM Researcher Explores Cognitive BPM

© 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

Cognitive is emerging as a new computing paradigm
Tabulating
Systems Era
Programmable Systems Era
Cognitive
Systems Era

Cognitive Era
4
Discovery & Recommendation
Probabilistic
Big (Dark) Data
Natural Language as the Interface
Intelligent Options

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

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

The Future of Work is Cognitive
7
The Evolution of Collaboration Technology In the Enterprise
The Rise of
Intelligent
Personal
Assistant

Intelligent Assistance and Related Technology – App Landscape
8
IPSoft’s
Amelia

We have seen just the tip of the iceberg…
9
Gartner Technology
Hype Cycle - 2015

How service computing continues to transform software
engineering, and how service computing needs to evolve?
The Future of Services is Cognitive…!
10

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

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

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

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.

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

THE JOURNEY TO COGNITIVE BPM
How advances in Cognitive Computing (AI and Machine Learning) impacts BPM?
16

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

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

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
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
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
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
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
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
Interface
Layer
Web
Service
XML
API
BackendSystems
Layer
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
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
Interface
Layer
Web
Service
XML
API
XML
API
BPMS
BackendSystems
Layer
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
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
Interface
Layer
Web
Service
XML
API
BackendSystems
Layer
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
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
Interface
Layer
Web
Service
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

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

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

Cognitive BPM Lifecycle
22
Cognitive
BPMS
Define
Enact
Monitor
Analyze
Next Steps, Adapt
Interact
Sense
Learn,
Discover
To
Traditional BPM Cognitive BPM

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.

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

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.

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
Inbox - Verse Highlighting actionable statements Recommending fulfilment actions
IBM Insight 2015 – The session on “Given your collaboration tools a brain”

28
IBM Insight 2015 – The session on “Given your collaboration tools a brain”
Invite/Calendar Action Archetype Automated Invite Parameters Extraction Calendar Entry Creation

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

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
https://sites.google.com/site/cbpm2017/

QUESTIONS?
Thank You!
32
Hamid R. Motahari Nezhad
IBM Almaden Research Center
Contact: motahari@us.ibm.com
Website: http://hamidmotahari.info
Twitter: @hamidmotahari

IBM Researcher Explores Cognitive BPM

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