International Business Machines Corporation

Microsoft Technology Licensing, LLC

Smitkumar Narotambhai Marvaniya

Vijay Ekambaram

Namit Kabra

Shantanu Ravindra Godbole

Shikhar Kwatra

Ritesh Kumar Gupta

Renuka Sindhgatta

Padmanabha Venkatagiri Seshadri

Clement Decrop

Bikram Sengupta

Caleb Miles

Tejas Indulal Dhamecha

Sarbajit Kumar Rakshit

Gandhi Sivakumar

Komminist S Weldemariam

Sushain Pandit

Timo Kussmaul

Hemant Kumar Sivaswamy

Diwesh Pandey

Malolan Chetlur

Uwe Karl Hansmann

Thomas Stober

Shinoj Zacharias

Umamaheswari C Devi

Ranjini Bangalore Guruprasad

Kedar Kulkarni

Jitendra Singh

Sneha Mondal

Aaron K Baughman

Zachary A Silverstein

Yannick Saillet

Mukesh Kumar Mohania

Saravanan Sadacharam

Ashok Pon Kumar Sree Prakash

Jagabondhu Hazra

Arun Joseph

Mohit Jain

Manikandan Padmanaban

Luke Peter Macura

Vikas Raykar

Maria Chang

Ankur Tagra

Komminist Weldemariam

Tathagato Bose

Kunjavihari Madhav Kashalikar

Geeth Ranmal De Mel

Sumanta Mukherjee

Otis Smart

Shiladitya Ghosh

Richard J Tomsett

Ayush Jain

Sujoy Kumar Roy Chowdhury

Subarna Ghosh

Shazia Afzal

Bharat Kumar Jain

Apparsamy Perumal

Dean Daskalantonakis

Wesley Richards

Kovit Nisar

Prashant Sharma

Syed Shahid Ali Afser

Divgian Sidhu

Ron Reuben

Zishan Sami

G Vinay Shashidhar

Sridhar Thiruvengadam

Osaid Rehman Nasir

An embodiment includes analyzing text content of a user query to identify via natural language processing (NLP) a query topic. The embodiment maps the query topic to a topic cluster at a node of a hierarchical model of a text database. The embodiment generates query demand data indicative of demand for the topic cluster based on user queries. The embodiment identifies the topic cluster as a topic-cache candidate based on the query demand data. The embodiment compares an amount of memory required for storing text associated with the first topic cluster to available cache memory. The embodiment caches the text of the topic cluster candidate upon determining that there is sufficient available cache memory space.

Caching of text analytics based on topic demand and memory constraints

From a first model parameter, an autoencoder network is generated. A reconstruction error for the autoencoder network is measured, the reconstruction error comprising a difference between an input to the autoencoder network and a corresponding output from the autoencoder network, the input to the autoencoder network comprising a portion of an initial set of data. The reconstruction error and a confidence score corresponding to a complexity level of the autoencoder network are aggregated into a level of difficulty score of the autoencoder network. From the level of difficulty score and an initial data access policy level corresponding to the initial set of data, a derived data access policy level corresponding to the initial data access policy level is generated, the derived data access policy level enforcing access to a transformed set of data generated by applying a transformation to the initial set of data.

Automatically adjusting data access policies in data analytics

Media is streamed in accordance with carbon footprint considerations. A streaming history of a streaming plan is analyzed to determine a historical carbon footprint. At least one streaming plan is presented that includes target carbon footprint relative to the historical carbon footprint. A selection is received for one of the at least one streaming plan, wherein the streaming performance on the streaming plan is tracked for post streaming plan carbon emissions. The user streaming performance is modified for the post streaming plan carbon emissions to substantially match the target carbon footprint of the at least one streaming plan.

Optimizing carbon emissions from streaming platforms with artificial intelligence based model

One embodiment provides a method, including: training a machine-learning model to produce customized farming practices specific to a farm to increase crop yield; wherein the training includes obtaining, from remote sensed data, (i) information corresponding to a crop of each of a plurality of farms and (ii) information corresponding to farming practices of each of the plurality of farms; wherein the training further includes detecting, from the remote sensed data, geographical features and farming characteristics of each of the plurality of farms; wherein the machine-learning model identifies from relationships between (iii) crop information and farming practices and (iv) geographical features and farming characteristics; and discovering, for a specific farm in an identified geographical location, utilizing the trained machine-learning model, and from farm-specific remote-sensed data, farming practices.

Discovering farming practices

Technologies for language agnostic OCR extraction include identifying a word region of an image using optical character recognition, applying a language agnostic machine learning model to the word region, where the language agnostic machine learning model is trained on training data including a set of image-text pairs and a set of multilingual text translation pairs, receiving, from the language agnostic machine learning model, a word region embedding that is associated with the word region, searching a multilingual index for a text embedding that matches the word region embedding, receiving, from the multilingual index, text associated with the text embedding; and outputting at least one of the text or the text embedding to at least one downstream process, application, system, component, or network.

Language-agnostic OCR extraction

A processor may receive a route request from a user. The processor may receive data associated with predicted routes of one or more nearby vehicles. The processor may select a recipient vehicle from the one or more nearby vehicles, where the recipient vehicle is selected based on recipient selection criteria. The processor may determine one or more driving change requests for the recipient vehicle. The processor may send the one or more driving change requests and a proposed token to the recipient vehicle. The processor may provide a token to the recipient vehicle in response to the recipient vehicle implementing the driving change requested.

Cooperative operation of vehicles

Targeted acquisition of data for model training includes identifying attributes of classified samples of a collection of samples classified by a classification model, and generating at least one query based on the identified attributes, the at least one query tailored, based on the attributes, to retrieve additional training data for training the classification model to more accurately classify samples and avoid incorrect sample classification.

Targeted data acquisition for model training

A computer hardware system having an image modification platform includes a hardware processor configured to initiate the following executable operations. A digital image is received by the image modification platform. A plurality of objects within the digital image are detected using an object segmentation engine of the image modification platform. The plurality of objects are classified using an object classification engine of the image modification platform and involve associating a stakeholder, respectively, to at least two of the plurality of objects. A knowledge map for the digital image is generated based upon the classifying and the stakeholders using a knowledge map engine. A set of constraints are identified for each of the plurality of objects based upon the knowledge map and the plurality of stakeholders. A modified digital image to be published is generated by an interactive image generation engine using the sets of constraints.

Constraint-based multi-party image modification

One embodiment provides a method, including: receiving a plurality of consumer feedback comments regarding one of a plurality of agricultural food products, wherein each of the plurality of consumer feedback comments comprises information regarding a characteristic of a given agricultural food product, wherein each of the plurality of agricultural food products corresponds to an agricultural source producing an agricultural food product category; updating a rating of each of the plurality of agricultural food products based upon consumer feedback comments corresponding to a given agricultural food product, wherein the updating comprises aggregating the received consumer feedback comments with previously supplied consumer feedback comments for agricultural food products within the agricultural food product category of a given agricultural source; ranking the plurality of agricultural food products based upon the ratings of the plurality of agricultural food products, wherein the ranking comprises ranking the plurality of agricultural food products against other agricultural food products within an agricultural food product category and produced by different agricultural sources; and providing, to the agricultural source, at least one recommendation with respect to a farming practice implemented by the given agricultural source, wherein the recommendation is based upon the ranking of an agricultural food product produced by the given agricultural source.

Recommendations for farming practices based on consumer feedback comments and preference

Classification and visualization of user interactions with an interactive computing platform is provided by building and providing a graphical user interface (GUI) of graphical elements for display on a display device. The graphical elements present visualizations of user interactions between users and an interactive computing platform in progression of the users through tasks based on the user interactions. The graphical elements also present identified features of the user interactions relative to the tasks and progression of the users therethrough. The building and providing includes building and providing, for a task of the tasks, a task element with first features corresponding to subtasks of the task and second features providing relations between the first features.

Classification and visualization of user interactions with an interactive computing platform

Methods, systems, and computer program products for auto-discovery of reasoning knowledge graphs in supply chains are provided herein. A computer-implemented method includes obtaining a spatiotemporal query related to a demand of at least one product in a supply chain; analyzing the spatiotemporal query to identify one or more parameters affecting the demand of the at least one product, wherein the one or more parameters comprise at least one of one or more climate parameters and one or more disruptive event parameters; generating a knowledge graph comprising information indicating an impact on the demand of the at least one product for at least a portion of the one or more parameters; and outputting, to a user interface, an explanation of a predicted demand forecast for the at least one product based at least in part on the knowledge graph.

Auto-discovery of reasoning knowledge graphs in supply chains

Targeted acquisition of data for model training includes automatically generating metadata describing samples, of an initial dataset, in neighborhoods of an embedding space in which the samples are embedded. The samples described by the automatically generated metadata are classified by a classification model, and include both correctly classified samples in the neighborhoods and incorrectly classified samples in the neighborhoods. Additionally, attributes of one or more correctly classified samples of the collection of samples and one or more incorrectly classified samples of the collection of samples are identified, and queries are generated based on the identified attributes, the queries tailored, based on the attributes, to retrieve additional training data for training the classification model to more accurately classify samples and avoid incorrect sample classification.

An approach for modifying in real-time by removing or reinforcing stroboscopic effect from images associated with a viewing experience is disclosed. The approach includes identifying video clips, detecting environmental parameters and calculating display setting. The approach also analyzes display setting using recommendation from GAN, output displaying setting on an AR display and receiving feedback from user.

Visual experience modulation based on stroboscopic effect

A method includes determining a presence of one or more people around a user and determining, based on the presence of the one or more people, an exposure level. The method also includes receiving a message for the user, the message comprising a first portion and a second portion and removing, based on the exposure level, the first and second portions from the message to produce a first message. The method further includes removing, based on the exposure level, the first portion from the message to produce a second message and presenting, based on the exposure level, the first message to the user. The method also includes receiving, from the user, feedback about the first message and presenting the second message to the user in response to the feedback.

Tiered audio notifications based on surrounding context

Methods, systems and computer program products for automated learning are provided herein. A computer-implemented method includes authenticating a plurality of users for an automated learning session, wherein the plurality of users correspond to at least one device, and providing the automated learning session for the plurality of users. Providing the automated learning session comprises analyzing a plurality of learning models corresponding to one or more of the plurality of users, determining, based on the analysis, one or more activities to be performed by the plurality of users during the automated learning session, and executing the one or more activities on at least one device.

System and method for social learning utilizing user devices

Growing community of inventors

Bengaluru, India

Smitkumar Narotambhai Marvaniya