From Services to Cogs and Journey to Cognitive BPM

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

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

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!

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

From Services to Cogs and Journey to Cognitive BPM

Hamid Motahari

From Services to Cogs and Journey to Cognitive BPM