International Business Machines Corporation

Neelamadhav Gantayat

Sampath Dechu

Senthil Kumar Kumarasamy Mani

Giriprasad Sridhara

Pratyush Kumar

Monika Gupta

Anush Sankaran

Shreya Khare

Srikanth Govindaraj Tamilselvam

Vibha Singhal Sinha

Pankaj Dhoolia

Gargi Banerjee Dasgupta

Naveen Panwar

Saravanan Krishnan

Kushal Mukherjee

Avirup Saha

Naveen Eravimangalath Purushothaman

Dharma Teja Atluri

Rahul A R

Senthil K K Mani

Mechanisms are provided for automated generation of an electronic form for an electronic messaging subsystem. Historical conversation logs are obtained from the electronic messaging subsystem which comprise a plurality of communication sequences. Communication sequences within the historical conversation logs are clustered according to similarity of features. For each cluster the following operations are performed: identifying, within the cluster, sequences of normalized utterances that are repeated across communication sequences of the cluster; categorizing each sequence, in a set of the sequences of normalized utterances, as to whether the sequence can be represented as one or more electronic forms; and extracting, for each communication sequence in the cluster, attributes and corresponding attribute values. One or more electronic form data structures are generated based on the attributes and corresponding attribute values extracted for each communication sequence in the cluster.

Generating ChatOps based process orchestration flows

A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include receiving data and generating a contextual execution dependency graph with said data. The operations may include producing agents with said data and calculating an agent sequence for said agents based at least in part on said contextual execution dependency graph. The operations may include executing an automation script using said agent sequence and said contextual execution dependency graph.

Bootstrapping dynamic orchestration workflow

Methods, systems, and computer program products for multi-modal construction of deep learning networks are provided herein. A computer-implemented method includes extracting, from user-provided multi-modal inputs, one or more items related to generating a deep learning network; generating a deep learning network model, wherein the generating includes inferring multiple details attributed to the deep learning network model based on the one or more extracted items; creating an intermediate representation based on the deep learning network model, wherein the intermediate representation includes (i) one or more items of data pertaining to the deep learning network model and (ii) one or more design details attributed to the deep learning network model; automatically converting the intermediate representation into source code; and outputting the source code to at least one user.

Multi-modal construction of deep learning networks

One embodiment provides a method, including: receiving a process flow diagram element of a process flow diagram; identifying a context of the process flow diagram element, wherein the identifying a context comprises identifying (i) categories of elements connected to the process flow diagram element, (ii) swimlanes within the process flow diagram, and (iii) text included in the process flow diagram; encoding features of the process flow diagram element into a semantic vector, wherein the features are identified from the context of the process flow diagram element; and predicting, utilizing a process flow diagram model, a process flow diagram element for the process flow diagram based upon the at least one process flow diagram element, wherein the process flow diagram model receives and analyzes the features of the at least one process flow diagram and outputs the predicted process flow diagram element.

Process flow diagram prediction utilizing a process flow diagram embedding

One embodiment provides a method, including: obtaining a business process model representing a process flow having a plurality of steps for performing a business process, the business process model being a graphical representation of the process flow and including geometrical shapes representing activities of the process flow and edges representing a temporal ordering of the activities of the process flow; identifying important activities of the business process model; and generating a summary business process model from the business process model, wherein the summary business process model comprises nodes representing the important activities and excludes other nodes included within the business process model.

Summarizing business process models

One embodiment provides a method, including: obtaining at least one video capturing images of a writing capture device used during a business process design session, wherein the images comprise portions of the process flow; obtaining at least one audio recording corresponding to the business process design session; identifying an intended business process model shape; determining at least one business process model shape missing from the process flow provided on the writing capture device; identifying a task dependency for pairs of business process model shapes; and generating a business process model from (i) the intended business process model shapes, (ii) the at least one business process model shape missing from the process flow, and (iii) the identified task dependencies.

Generation of business process model

Systems and methods are provided to implement intelligent recommendations to users by modeling user profiles through deep learning of multimodal user data. For example, a recommendation computing platform collects multimodal user data from a computing device of a registered user, wherein the multimodal user data include time-series data, unstructured textual data, and multimedia data. A first deep learning classification engine is utilized to extract features from the multimodal user data. A second deep learning classification engine is utilized to generate a profile of the registered user based on the extracted features. A deep recommendation classification engine is utilized to determine a recommendation for the registered user based on the profile of the registered user, wherein the recommendation identifies at least one additional registered user. The recommendation is presented to the registered user on the computing device of the registered user.

Intelligent recommendations implemented by modelling user profile through deep learning of multimodal user data

Methods, systems and computer program products for natural language context embedding are provided herein. A computer-implemented method includes extracting a document anatomy and document elements from a given structured document, identifying semantic references in the given structured document, and generating an ontology comprising (i) a hierarchy of concepts and (ii) relations connecting the concepts, each concept comprising attributes for a document element. The computer-implemented method also includes generating natural language text context for a given document element by utilizing the ontology to combine (i) attributes of a given concept corresponding to the given document element with (ii) attributes of another concept, the other concept corresponding to another document element, the other concept being connected to the given concept by at least one relation. The computer-implemented method further includes modifying the given structured document by embedding the natural language context with the given document element in the given structured document.

Embedding natural language context in structured documents using document anatomy

One embodiment provides a method, including: receiving, from a user and at a user interface of a conversational agent, a query related to a business process; identifying, using process entity extraction on the query, (i) the business process and (ii) a business object corresponding to an entity of the query; mapping the business object to code corresponding to the business object, wherein the mapping comprises (i) mapping the business object to an object within a business process model using a domain dictionary and (ii) accessing code corresponding to the object within the business process model; generating a natural language response responsive to the received query by (i) extracting the code corresponding to the business object, (ii) identifying a rule within the extracted code corresponding to a variable of the query, and (iii) generating the natural language response from the identified rule; and providing the natural language response.

Provision of natural language response to business process query

One embodiment provides a method, including: receiving input of a learning session that is being conducted by an educator, being provided to at least one user, and being related to a subject; determining, using a knowledge base, that at least one topic relevant to the subject of the learning session is incomplete, wherein the determining comprises building a knowledge subgraph of the learning session and comparing the built knowledge subgraph to at least a portion of the knowledge base; generating at least one question to be asked of the educator relevant to the at least one incomplete topic; identifying, using at least one natural language text classifier model, a location within the learning session to ask the generated at least one question; and providing, to the educator, an output corresponding to the at least one question at the identified location within the learning session.

Question generation for learning session

One embodiment provides a method, including: utilizing at least one processor to execute computer code that performs the steps of: using an electronic device to engage in an interactive session between a user and a virtual assistant; receiving, at the electronic device, audio input from the user, wherein the audio input comprises a problem-solving query corresponding to a request by the user for assistance in solving a problem related to at least one object; parsing the audio input to identify at least one annotated video file corresponding to the at least one object and the problem-solving query; determining a state of the object and a location in the at least one annotated video file corresponding to the state of the object; and providing, to the user and based on the location in the at least one annotated video file, instructional output related to the problem-solving query.

Interactive voice based assistant for object assistance

One embodiment provides a method, including: receiving a natural language query; selecting a disambiguation state model representing conversational dialog history, wherein the disambiguation state model comprises a plurality of nodes representing an entity, and a plurality of edges representing a path between two of the plurality of nodes, each of the plurality of edges including an assigned weight; traversing, the disambiguation state model using the natural language query to select a path to one of the plurality of nodes and providing the user the entity associated with the one of the plurality of nodes and iteratively selecting paths and nodes based upon input received from the user until a final node of the disambiguation state model is reached; providing a response to the natural language query based upon the entity of the final node; and updating the disambiguation state model based upon the traversed paths and nodes.

Adaptive conversational disambiguation system

One embodiment provides a method for generating a process learning graph and a document output from a recorded process for training a cognitive agent, the method comprising: utilizing at least one processor to execute computer code that performs the steps of: obtaining a recording of a process, wherein the recording comprises a demonstration of executing the process; generating, using the recording, the process learning graph, wherein the process learning graph identifies a process flow; generating, using the recording, the document output, wherein the document output comprises process screen transitions and process steps; and providing the process learning graph and the document output to the cognitive agent. Other aspects are described and claimed.

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Bengaluru, India

Neelamadhav Gantayat