International Business Machines Corporation

Hewlett-packard Development Company, L.p.

Instart Logic, Inc.

Sesame Workshop

Prasenjit Dey

Vijay Ekambaram

Ravindranath Kokku

Ruhi Sharma Mittal

Satyanarayana Venkata Nitta

Sriganesh Madhvanath

Hariharan Kolam

Manav Ratan Mital

Raghu Batta Venkat

Aditya Sinha

Abhishek Marwah

Tamer E Abuelsaad

Nitendra Rajput

James P Gray

Chalapathy Venkata Neti

Bikram Sengupta

Anbumani Subramanian

Ramadevi Vennelakanti

Miles Ludwig

Emily Reardon

Mukesh Kumar Mohania

Malolan Chetlur

Ramasuri Narayanam

Saravanan Sadacharam

Sharad C Sundararajan

Anjaneyulu Seetha Rama Kuchibhotla

Vikas V Joshi

Robert J Schloss

Dipyaman Banerjee

Serene Banerjee

Shekhar Ramachandra Borgaonkar

Sitaram Ramachandrula

Jodi M Cutler

Pulkit Parikh

Noushath Shaffi

Allison C Allain

Nick Bartzokas

Michelle Newman-Kaplan

Aaron K Baughman

Shikhar Kwatra

Padmanabha Venkatagiri Seshadri

Palanivel Andiappan Kodeswaran

Patrick D Watson

Maria Chang

Jae-Wook Ahn

Krishnan Ramanathan

Dinesh Mandalapu

Praphul Chandra

Pritesh Patel

Prashanth Anant

Sonia Soubam

Rama Vennelakanti

Deepu Vijayasenan

Mohak K Sukhwani

Ravi Kokku

Sheetal Manan Sureka

Anand Govindara

Sharad Chandra Sundararajan

An exemplary method includes obtaining a group of classification labels and corresponding confidence values for at least one object identified within an image using a computer-based object recognition technique; generating a conversation, to resolve ambiguity among the classification labels, the generating including iteratively performing the following when (i) each of the confidence values is below a threshold value or (ii) two or more of the confidence values are above the threshold value: using a wordweb to identify properties that distinguish between a first one and a second one of the labels, ranking the properties; selecting the property having the highest rank to generate a question, and filtering at least one of the first and second label based on user input received in response to the question; and when only one of the confidence values exceeds the threshold value, classifying the object using the label corresponding to the one confidence value.

Image object recognition through multimodal conversation templates

A method, a structure, and a computer system for tangible mixed reality. Exemplary embodiments may include identifying one or more real objects within an environment and identifying one or more interactions between the one or more real objects, one or more reactive objects, and one or more animations therebetween. The exemplary embodiments may further include generating one or more learning activities for a user of a mixed reality headset that include at least one of the one or more interactions, and deploying the one or more learning activities to the mixed reality headset.

Dynamic interaction deployment within tangible mixed reality

Methods, systems, and computer program products for image object disambiguation resolution are provided herein. An example of a method includes: obtaining a group of classification labels and corresponding confidence values for an object in an image; using a wordweb to determine one or more properties that distinguish between at least a first one of the classification labels and at least a second one of the classification labels within the group; selecting a first property from the properties to generate a question based on information indicating a level of prior knowledge of the user with each of the properties and each of the one or more labels; assigning a belief score to an answer; and determining whether to present at least a second question to verify the first answer based on a comparison of the belief score to a belief threshold value.

Image object disambiguation resolution using learner model based conversation templates

A computer-implemented method for a socially connectable agent. A non-limiting example of the computer-implemented method includes receiving, by a processor, input events. The method includes evaluating and enacting rules, by the processor, based on the received input events, where the rule include stochastic elements and temporal operators. The method pushes, by the processor, action events that result from the evaluation and enactment of the rules, and initiates, by the processor, action events.

Rules engine for social learning

Techniques for assessing the proficiency of artificial intelligence agents and users in a given knowledge domain are described. A plurality of proficiency agents can be initialized with a plurality of proficiency scores, by performing a plurality of assessments between pairs of proficiency agents selected from the plurality of proficiency agents. A first client device associated with a first user is matched with a first proficiency agent of the plurality of proficiency agents, based on a first proficiency score associated with the first user and a second proficiency score of the plurality of proficiency scores corresponding to the first proficiency agent. Assessments results of an assessment performed between the first client device and the first proficiency agent are received, and a rating system update function is used to update the first proficiency score and the second proficiency score, based on the assessment results.

Modeling mastery in distributed cognitive systems and storing the results in a ledger

Providing targeted, evidence-based recommendations to improve digital content quality, in one aspect, may include partitioning digital content into a sequence of logical content modules. Quality scores for the logical content modules along a plurality of quality dimensions may be determined. User interactions with the digital content on a user interface are monitored. Raw events generated from user interaction in accessing the digital content are captured. Raw event patterns for the logical content modules are detected. The raw event patterns of user interaction are transformed into a set of dynamic states representing potential user experience and performance. The set of dynamic states are associated with a respective logical content module. The quality dimensions of the logical content modules and associated quality scores are correlated with the dynamic states.

Providing targeted, evidence-based recommendations to improve content by combining static analysis and usage analysis

A combination of knowledge representation and retrieval, assessments and personalization for a tutoring platform that provides an automated phased word exposition method for vocabulary learning and enable learners to learn words (or any learning objective) with sufficient proficiency and without excessive repetition. The phased learning objective method implements a moving average model that models the learner across multiple dimensions (e.g. reading, listening, writing, speaking) such that the platform is confident of the learner's understanding, while using minimal assessment doses, and such that the understanding transfers across experiences. The method implemented provides for multiple learning and assessment activities for a learning objective and leverages the same learner model for each learning dimension. Each word of a word set passes through learning and assessment-only phases for statistically significant evidence of learning. The model derived from the phased word expansion can be shared across applications, toys and tangibles that learners interact with.

Phased word expansion for vocabulary learning

Personalized video interjections based on a learner model and a learning objective. A method for adding interjections to a video includes analyzing the content of a plurality of videos based on a set of learning objectives, selecting a video based on a learning objective, determining types of video interjections using an analytics engine that compares a learner model and the learning objective, determining a location for the video interjections using the analytics engine, generating a video interjection for each video interjection type and inserting the video interjections into the video at the determined locations.

Personalized video interjections based on learner model and learning objective

Methods, systems, and computer program products for image object disambiguation resolution are provided herein. An example of a method includes: initiating a conversation for resolving ambiguity among a group of labels corresponding to an object in an image; using a wordweb to determine properties that distinguish between at least one first label and at least one second label within the group; selecting a first property from the properties to generate a question, wherein said selecting is based at least in part on a learner model of a user including information indicating a level of prior knowledge of the user with each of the properties and each of the one or more labels; assigning a belief score to an answer; and determining whether to present at least a second question to verify the first answer based on a comparison of the belief score to a threshold value.

A system for performing implicit assessment of a pupil is provided. The system receives a learning content from a content provider, the learning content having checkpoints at different stages of the learning content. The system associates assessment instructions with each checkpoint of the learning content. The system renders the received learning content for presentation to a pupil and captures the behaviors of the pupil during the presentation of the learning content. The system performs an assessment of the pupil at each checkpoint encountered during the rendering of the learning presentation. The assessment at a checkpoint includes using the checkpoint's associated assessment instructions to assign a score based on the captured behaviors. The system then produces an overall evaluation based on the assessments performed at the encountered checkpoints.

Computationally derived assessment in childhood education systems

An exemplary method includes initiating a conversation with a user for resolving ambiguity among a group of labels corresponding to an object in an image, wherein each label is associated with a confidence value; iteratively performing, when each of the confidence values is below a first threshold value, or two or more of the confidence values are above the first threshold value: using a wordweb to identify properties that distinguish between a first one of the labels and a second one of the labels, ranking the identified one or more properties; selecting the property having the highest rank to generate a question, and filtering at least one of the first label and the second label based on user input received in response to the question; and when only one of the confidence values exceeds the first threshold value, classifying the object using the label corresponding to the one confidence value.

Power-efficient data collection by a personal imaging device regarding a user's interaction with an application on a mobile device by automatically switching on and off costly, power-consuming applications of the personal imaging device without the use of a radio signal. The personal imaging device detects a visual start signal displayed on the mobile device of a tagged application on the mobile device. The personal imaging device activates sensors of the personal imaging device for collecting data regarding the user's interaction with the application on the mobile device. The personal imaging device collects data regarding the user's interaction with the application on the mobile device via the activated sensors and stores the data in a repository. The personal imaging device detects a visual end signal displayed on the mobile device, ending data collection.

Power and processor management for a personal imaging system based on user interaction with a mobile device

Methods, systems and computer program products for evaluating contributions in collaborative environments are provided herein. A computer-implemented method includes obtaining indications of tasks of a given workload that are completed by respective ones of a plurality of client devices. The method also includes identifying first and second tasks completed by first and second client devices at first and second times, determining whether the first task provides a clue for the second task and whether the second time is within a designated time window starting after the first time, and analyzing eye-gaze information to determine whether a second user of the second client device observed a first user of the first client device performing the first task. The method further comprises modifying contribution scores for the first and second users responsive to the determinations.

Evaluating user contribution in collaborative environments

Methods, systems, and computer program products for determining a personalized advertisement channel are provided herein. A computer-implemented method includes processing multiple data sources associated with a user based on data derived from multiple notification messages accessed via an operating system feature of a mobile device of the user; determining a current state in connection with a product and/or service based on said processing, wherein said current state comprises a current likelihood that the user will purchase the product and/or service; computing probabilities, conditioned on each of multiple advertisement media, that the user will transition from a first state to a second state based on said processing; and identifying one of the advertisement media to implement with the product and/or service to transition the user from the current state to a separate user state, wherein said identifying is based on (i) the current state and (ii) the multiple computed probabilities.

Determining a personalized advertisement channel

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Bengaluru, India

Prasenjit Dey