International Business Machines Corporation

Merlyn Mind, Inc.

Xio Research, Inc.

Aditya Vempaty

Sharad C Sundararajan

Ravindranath Kokku

Satya Nitta

Shom Ponoth

Ashish Jagmohan

Elham Khabiri

Tamer Abuelsaad

Matthew Daniel Riemer

Vadim Sheinin

Ta-Hsin Li

Payel Das

Sophia Krasikov

Harini Srinivasan

Robert C Smith

Jason Boada

Swapna Buccapatnam Tirumala

Robert Hutter

Flavio D Calmon

Srirupa Chakraborty

Satyanarayana Nitta

Tamer E Abuelsaad

Krishna Chaitanya Ratakonda

Richard Thomas Goodwin

Richard Baxter Hull

Fenno F Heath, Iii

Flavio Du Pin Calmon

Paul V Haley

Prasenjit Dey

Rajkumar Chandrasekaran

Lorin F Wilde

Mohammad Niknazar

Amol Nayate

Javier Villafana

Robert Smith

Latha Ramanan

Juan Ernesto Penarrieta Cornejo

Helene Alonso

A component management server computer ('server') and processing methods are disclosed. In some embodiments, the server is programmed to continuously receive input data regarding what is happening in the physical room from one or more input devices. The server is programmed to then detect an utterance of a spoken word from the input data and generate one or more sound metrics based on the input data. Based on the sound metrics as applied to certain criteria, the server is programmed to activate a component, such as an input device, variable, software system, or output device, and cause one or more output devices to execute an action that alerts a user of the activated component. The server can also be programmed to turn on, off, up, or down any of the components based on the activated component.

Automatic adaptation of multi-modal system components

A method comprises receiving from an input device, a capture of user action as an initial command; interpreting the initial command into an interpreted command; generating a first set of modified commands that are based on the interpreted command, including: a first modified command that has a phonetic similarity to the interpreted command within a certain threshold, and a second modified command that is semantically related to an earlier command; transmitting, to an output device, the first set of modified commands; receiving a response to a group of commands including the first set of modified commands; recording an identifier of an input device from which the response was received and a type of the response in a log; when the response includes acknowledging a specific command of the group of commands as an accepted command, executing, the accepted command; otherwise, generating a second set of modified commands.

Multimodal intent entity resolver

A data management server computer ('server') is disclosed. The server receives action data associated with one or more users identifying a plurality of actions performed through one or more computer devices in a multi-modal networked environment, identifies useful sequences of actions as compound actions, and generates deep links representing the compound actions as UI elements which when invoked cause execution of the represented compound actions.

Generating compound action links in a multi-modal networked environment

A system, apparatus and a method for determining distinguishable data, includes processing input data into a plurality of elements, calculating distinguishability of the plurality of elements using phonetic vowels, and determining distinguishable elements from among the plurality of elements, according to the distinguishability calculation.

Vowel based generation of phonetically distinguishable words

Techniques for mapping policy documents to regulatory documents to check for compliance between the policies and documents are provided. In one example, a computer-implemented method determining, by a system operatively coupled to a processor, an information input, a control framework, and a document from a first group consisting of a regulatory document and a policy document, wherein the information input is a corpora from a second group consisting of a domain corpora and a global corpora. The computer-implemented method can also comprise mapping, by the system, the received regulatory document or the received policy document to the control framework using a supervised machine learning technique.

Facilitating mapping of control policies to regulatory documents

Interpretation of user commands is accelerated through digital user interfaces of various modalities, including generation and presentation of command modifications for rapid correction of incomplete or erroneous user commands. An embodiment detects whether the interpreted command is accurate and, if inaccurate, precisely what was the intended command or, at least, what suggested modification to the interpreted command would be sufficient to match the intent of the user. Disambiguation occurs that entails multiple recommendation generators proposing modified commands that may more accurately reflect the intent of the user. The user may provide a response that is either a confirmation of which one of several modified commands that were automatically proposed does the user intend or a correction that computer device may use to filter or replace currently offered modified commands to generate improved modified commands. By iterative refinement, the user and computer device may quickly agree on an accurate command that the computer device should execute.

A method and system are provided to calculate a future behavioral data and identify a relative causal impact of external factors affecting the data. Behavioral data and data for one or more external factors are harvested for a first time period. New behavioral data is harvested for a second time period. New data for the second time period is harvested. Based on a second training algorithm, a forecast time series value of a future behavioral data for a third time period that is after the second time period is calculated. A relative causal impact between each external factor and the predicted time series value of the behavioral data, for the third time period, is identified.

Time series forecasting to determine relative causal impact

A device management server computer ('server') and processing methods are disclosed. In some embodiments, the server is programmed to manage a plurality of input devices and output devices in a physical room. The server is programmed to analyze media data capturing actions performed by a user in real time as a participant in the physical room, determine how the user would like to connect at least one of the input devices and one of the output devices from the analysis, and enable the connection. The sever is programmed to interpret the actions and derive commands for connecting two or more devices based on predetermined data regarding the input devices and output devices and rules for referring to and connecting these devices.

Managing connections of input and output devices in a physical room

Sensory input data signal is communicated to an AI platform and translated into a corresponding scenario. The translation is directed at an associated force and application of the force to a selected or identified object and environment. Real-time analysis of the force is applied, which includes modeling an expected behavior. An assessment response is received and compared to a corpus. A solution in the corpus proximal to the assessment response is identified, and a reaction of proximity of the response to the identified solution is created. Proximity data is converted to a sensory output signal. Receipt of the sensory output signal by a corresponding sensory output device creates a physical manifestation of generated feedback of the reaction data to a sensory medium. Embodiments are directed at both learning and assessment, and leverage a corpus with the AI platform in support of an interactive learning experience.

Interactive feedback and assessment experience

A method for generating an output comprising one or more segments includes obtaining a plurality of profiles derived from unstructured data associated with a plurality of users, wherein a given one of the profiles corresponds to a respective one of the users; repetitively executing at least one machine learning technique on the plurality of profiles, each execution producing a respective set of one or more segments from the plurality of profiles; generating a complete graph by performing pairwise comparisons between sets of segments from respective executions; applying at least one persistency graph algorithm to the complete graph to find one or more coherent clusters comprising one or more segments that are persistent across the repetitive executions of the machine learning technique; and producing the output at least in part by selecting at least one of the segments from at least one of the coherent clusters.

Automatic segmentation of a collection of user profiles

Embodiments relate to a system, program product, and method for use with an intelligent (AI) computer platform to explore real-world modeling. A sensory input data signal is communicated to the AI platform and translated into a corresponding scenario. The translation is directed at an associated force and application of the force to a selected or identified object and environment. A reaction to the application is created, and reaction data is converted to a sensory output signal. Receipt of the sensory output signal by a corresponding sensory output device creates a physical manifestation of generated feedback of the reaction data to a sensory medium. The embodiments are directed at both learning and assessment, and leverage a corpus with the AI platform in support of an interactive learning experience.

Interactive learning experience

A device management server computer ('server') is programmed to manage a plurality of input devices and output devices in a physical room. The server is programmed to analyze media data capturing actions performed by a user in real time as a participant in the physical room, determine how the user would like to connect at least one of the input devices and one of the output devices from the analysis, and enable the connection. The sever is programmed to interpret the actions and derive commands for connecting two or more devices based on predetermined data regarding the input devices and output devices and rules for referring to and connecting these devices.

Techniques for improving products based on data-driven models are provided. In one example, a system comprises a receiving component that receives product data representing information about a set of products, wherein a first product of the set of products comprises a first combination of a first set of ingredients, and wherein the product data comprises product composition data representing a composition of the first product. The system further comprises a learning component that generates product space data representing a product space that characterizes the set of products and respective degrees of similarity between members of the set of products, wherein a degree of similarity between the first product and a second product of the set of products is determined based on product distance data representing a determined distance metric resulting from a comparison of the first set of ingredients to a second set of ingredients combined to produce the second product.

Data-driven models for improving products

Growing community of inventors

Elmsford, NY, United States of America

Aditya Vempaty