International Business Machines Corporation

Yara Rizk

Vatche Isahagian

Yasaman Khazaeni

Vinod Muthusamy

Tathagata Chakraborti

Sebastian Carbajales

Allen Vi Cuong Chan

Shubham Agarwal

Dario Andres Silva Moran

Salman Saleem Sheikh

Burak Aksar

Zilu Tang

James Johnson

Sampath Dechu

Jaydeep Sen

John Henry Green

Rania Yousef Khalaf

Merve Unuvar

Tarun Tater

Arunima Chaudhary

Scott Boag

Qian Pan

Kushal Mukherjee

Siyu Huo

Prabhat Maddikunta Reddy

Falk Pollok

Michelle Brachman

Yazan Obeidi

Amanda Booth

Sarath Sreedharan

Calvin Phung

Sonali Dey

Shayne Yi Hsien Lin

Isabella Olivares

Ossama Mahmoud

Jamil Tahsin Samouh

Mahmoud Mahmoud

Nikhil Bhambra

Lionel Wu

Zhongshen Zeng

Lauren Allison Goldstein

Kylee Mikel Barnard

Shay Hall

Varun Sangwan

A data visualization method, system, and computer program product that includes identifying an intent from a natural language query in a conversation with a conversational system, utilizing verbiage from the natural language query and the intent to determine one or more data columns for visualization of results of the natural language query, and displaying a visualization of the determined one or more data columns.

Ontology-based data visualization

An embodiment records execution of a workflow comprising a skill, the recording generating workflow data. An embodiment selects, using the workflow data, an ontology tree having above a threshold amount of similarity to the workflow. An embodiment constructs, using the ontology tree and the workflow data, a first skill tree corresponding to the workflow. An embodiment integrates, into an existing skill tree of an application, the first skill tree, the integrating resulting in an integrated skill tree of the application. An embodiment executes, responsive to an intent requesting execution of the skill using new data, using the integrated skill tree and the new data, the skill.

Ontology based workflow automation and execution

An embodiment for generating multi-turn dialog datasets for training of dialog or conversational agents. The embodiment may select an agent from a set of agents. The embodiment may automatically identify sentences from training data of the selected agent that satisfy a first sequential node condition of the selected random dialog node. The embodiment may automatically determine an approach for responding to the first sequential node condition of the selected random dialog node that either satisfies the first sequential dialog node condition, or inserts a multi-turn conversational property, and generate a corresponding response. The embodiment may automatically determine additional approaches for responding to each condition within subsequent sequential child nodes of the selected random dialog node that either satisfy each subsequent sequential child node condition or insert a multi-turn conversational property, and generate corresponding responses. The embodiment may collect and store data relating to the selected agent and the generated responses.

Generating multi-turn dialog datasets

Embodiments of the invention are directed to a programmable computer system that includes a processor system operable to perform processor system operations. The processor system operations include using a workflow composer to perform an automated workflow composition process that generates a composed workflow that is operable to, when executed by a host device, satisfy a target logical goal. Performing the automated workflow composition process includes using a workflow-metric model to control the automated workflow composition process such that the composed workflow is operable to, when executed by the host device, satisfy the target logical goal in a manner that optimizes a target metric goal. The target metric goal quantifies a performance feature of the composed workflow.

Automated workflow composer using unmodeled metric-based goals

A natural language input is communicated to a back end application. Candidate agent data communicated from the back end application is presented. The candidate agent data comprises data of a set of agents considered by the back end application for performance of a command extracted from the natural language input. Candidate agent score data communicated from the back end application is presented. The candidate agent score data comprises score data of an agent in the set of agents. Selected agent data communicated from the back end application is presented. The selected agent data comprises data of a selected agent in the set of agents, the selected agent selected by the back end application for performance of the command. Skill performance data communicated from the back end application is presented. The skill performance data comprises data of a processing step performed by the selected agent.

Conversational system action presentation

A query can be received from a user. The query can be sent to a plurality of automated agents to process the query. Results and associated confidence scores can be received from the plurality of automated agents. At least some of the results and associated confidence scores can be probed, based at least on a reason given for a result having the highest associated confidence score among the received results and associated confidence scores, to select an automated agent from the plurality of automated agents for answering the query. Information can be stored, where the information can include at least the results and associated confidence scores and a selected automated agent for answering the query, where at least one of the plurality of automated agents learns from the stored information to update its confidence score in answering the query.

Adaptive answer confidence scoring by agents in multi-agent system

A method, computer program, and computer system are provided for explaining generation of a flow from natural language utterances. Data corresponding to a natural language utterance is received. One or more constraints corresponding to a flow to be generated are determined based on the received natural language utterance. A flow is constructed based on the determined constraints. An explanation associated with the constructed flow is provided, and the explanation identifies parameters corresponding to constructing the flow.

Explaining natural-language-to-flow pipelines

A method, computer system, and a computer program product for generating a conversational bot for an application programming interface (API) is provided. The present invention may include parsing an API schema. The present invention may include generating sentences for the conversational bot from the parsed API schema. The present invention may include constructing the conversational bot by training a deep learning model. The present invention may include receiving a natural language expression from a user. The present invention may include determining whether the natural language expression is enough to activate the bot.

Generating and updating conversational artifacts from APIS

Aspects of the present disclosure relate to audio/video (A/V) stream functionality verification. A stream segment of a video feed prior to transmission over a network as captured by a transmitting device within a web-based conference can be stored. A stream segment of the video feed after transmission over the network as received by a receiving device within the web-based conference can be stored. The stream segment of the video feed prior to transmission over the network can be compared with the stream segment of the video feed after transmission over the network to determine a video feed quality.

Audio/video (A/V) functionality verification

A method for automatically detecting and processing a computer input event comprising one or more intents is provided. The method may include, in response to receiving the computer input event, automatically generating a parse tree comprising nodes including a possible interpretation of the received computer input event and at least one determined intent. The method may further include performing a confidence evaluation for the nodes and receiving one or more first scores from one or more computer agents for a respective node to indicate an ability of a respective computer agent to process the respective node. The method may further include, determining a second score for the respective node by aggregating the one or more first scores from the one or more computer agents for the respective node. The method may further include, based on the determined second score, selecting a node for processing the received computer input event.

Multi-agent chatbot with multi-intent recognition

Computer-implemented methods, computer program products, and computer systems for improving intent determination in a messaging dialog manager system. The computer-implemented method for improving intent determination in a messaging dialog manager system may include one or more processors configured for receiving first agent entry data corresponding to a first agent communicating in a messaging dialog interface, determining that the first agent entry data expects a response that is within a first response domain, determining that a first user entry entered in the messaging dialog interface is not within the first response domain. Further, the computer-implemented may include identifying a second agent configured with a second response domain that includes the first user entry and transmitting the first user entry to the second agent to facilitate a seamless transition of an established communicational flow between the first agent and a first user.

Intent determination in a messaging dialog manager system

A conversational interface generation method, system, and computer program product that includes determining a conversational artifact for a computer program from a specification of the computer program and generating a conversational interface for the computer program based on the conversational artifact for the computer program included in the specification.

Auto generation of conversational artifacts from specifications

A method, a computer program product, and a computer system orchestrate devices of a device ecosystem. The method performed by an orchestrator includes receiving polling data from agents that are directly connected to the devices indicating simulations run by the devices for a cloud service. The method comprises determining a preview state of the device ecosystem. The method comprises determining an action to change a state of the device ecosystem based on the preview state. The method comprises generating an instruction for the action and the devices to perform the action. The method comprises transmitting the instruction to the agents, the agents actuating the change via the determined at least one of the devices performing the action. The orchestrator orchestrates the agents such that the devices are provided the cloud service isolated from a cloud computing system associated with the cloud service.

Internet of things device orchestration

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Cambridge, MA, United States of America

Yara Rizk