International Business Machines Corporation

Siemens Medical Solutions Usa, Inc.

Vikas Raykar

Raghavendra Singh

Amrita Saha

Ayushi Dalmia

Priyanka Agrawal

Nupur Aggarwal

Sachindra Joshi

Srikanth Govindaraj Tamilselvam

Pankaj Satyanarayan Dayama

Noam Slonim

Abhishek Bansal

Sumanta Mukherjee

Kushagra Manglik

Vijay Ekambaram

Xiang Sean Zhou

Ashish Verma

Amith Singhee

Ehud Aharoni

Gerardo Hermosillo Valadez

Ashok Pon Kumar Sree Prakash

Smitkumar Narotambhai Marvaniya

Shantanu Ravindra Godbole

Yiqiang Zhan

Indrajit Bhattacharya

Vivek Shyamlal Sharma

Yonatan Bilu

Kedar Kulkarni

Zhigang Peng

Dan Gutfreund

Maneesh Dewan

Ruty Rinott

Satyam Dwivedi

Daniel Hershcovich

Surya Shravan Kumar Sajja

Jaikrishnan Hari

Mitesh Khapra

Digbalay Bose

Godbole Shantanu

Methods, systems, and computer program products for determining locations for offerings using artificial intelligence are provided herein. A computer-implemented method includes determining an initial sub-set of one or more enterprise locations, from a set of multiple enterprise locations, for providing at least one enterprise offering; determining at least one sequential ordering of one or more additional ones of the multiple enterprise locations for providing the at least one enterprise offering by processing data related to the initial sub-set of one or more enterprise locations using an artificial intelligence engine that utilizes one or more artificial intelligence techniques; and automatically training at least a portion of the artificial intelligence engine that utilizes the one or more artificial intelligence techniques based at least on feedback data pertaining to provision of the at least one enterprise offering to the at least one sequential ordering of one or more additional enterprise locations.

Determining locations for offerings using artificial intelligence

Methods, systems, and computer program products for facilitating user selection using trend-based joint embeddings are provided herein. A method includes obtaining a selection of an item in an online catalog; determining a compatible item of the plurality of items at least in part by providing the selected at least one item and at least one previously selected item corresponding to the user to a trend-based machine learning model, wherein the trend-based machine learning model is trained on historical data associated with the item in the online catalog and fine-tuned based on current trend data from multiple data sources; receiving feedback in response to outputting the at least one compatible item; identifying one or more attributes related to the at least one compatible item based on the feedback; and using the trend-based machine learning model to determine at least one additional compatible item based on the one or more attributes.

Facilitating user selection using trend-based joint embeddings

One embodiment provides a method, including: receiving information for each of a plurality of products; for each of the plurality of products: predicting a demand for the product; calculating a sustainability score, wherein the sustainability score is based upon a material composition of the product; and computing a similarity score, wherein the computing comprises comparing pairs of products within the plurality of products; generating a plurality of assortment choices from the plurality of products, wherein each of the plurality of assortment choices has a corresponding assortment demand value, assortment sustainability score, and assortment similarity score, wherein the generating comprises utilizing a multi-objective formulation based upon the predicted demand, the calculated sustainability score, and the computed similarity score; and providing the plurality of assortment choices to a user for selection.

Retail product assortment generation and recommendation

Methods, systems, and computer program products for customizing agricultural practices to maximize crop yield are provided herein. A computer-implemented method includes obtaining data pertaining to (i) a geographical area comprising a plurality of regions and (ii) one or more agricultural practices applied to the geographical area; assigning each of the plurality of regions to a respective cluster of a set clusters, based at least in part on comparing features identified in the data, wherein similar ones of said regions are assigned to the same cluster; generating instructions that are specific to a given cluster in the set, wherein the instructions relate to agricultural tasks to be performed on the regions assigned to the given cluster; and triggering, based on said instructions, one or more automated farming processing devices, thereby carrying out at least a portion of said agricultural tasks.

Customizing agricultural practices to maximize crop yield

Methods, systems, and computer program products for pressure-based apparel image searching are provided herein. A computer-implemented method includes converting images in a product catalog of an electronic commerce website to a predetermined representation; storing the converted images in an index; determining a first object of interest within an image derived from a social media post and displayed on a screen, by detecting physical contact imparted by a user at a position on the screen corresponding to where the first object of interest is located; quantifying the amount of pressure applied by the user via the physical contact; determining additional objects of interest within the image based on the amount of pressure applied by the user; retrieving, from the index, images of products corresponding to the first object of interest and images of products corresponding to the additional objects of interest; and displaying the retrieved images on the screen.

Touch and pressure-based apparel image searching

Aspects of the present invention disclose a method, computer program product, and system for recommending an apparel product is provided. The method for recommending an apparel product may include one or more processors identifying a user selection of an apparel product, wherein the apparel product includes information associated with retail websites. The method further includes one or more processors categorizing the apparel product into one or more groupings based upon user preferences, wherein the grouping is based on available versions of the apparel product. The method further includes one or more processors determining current apparel preference information corresponding to the user based on user preferences, fitness application data, health monitoring application data, and user-provided measurements. The method further includes one or more processors determining a recommended garment of the identified user-selected apparel product based on mapping the determined apparel preference information to product specifications of the identified user-selected apparel product.

Determining apparel-based recommendations for a user

Methods, systems, and computer program products in connection with a system for bidding on multi-modal sub-spaces for e-commerce searches is provided herein. A computer-implemented method includes representing, in an embedding space affiliated with at least one electronic commerce website: (i) images related to one or more bids and (ii) items of text related to the one or more bids, the images and the items of text both coming from one or more bidding entities. Such a method also includes mapping a query to the embedding space based on a comparison of features of the query to (i) the images and (ii) the items of text. Further, such a method additionally includes selecting at least one of the one or more bidding entities based on the mapping, and outputting, to at least one user in response to the query, search results associated with the at least one selected bidding entity.

Method, computer program product and system for bidding on multi-modal sub-spaces for e-commerce searches

Methods, systems, and computer program products for generating metadata for image-based querying are provided herein. A computer-implemented method includes processing a query image against a database by applying a deep learning visual model to the query image and images contained within the database; retrieving a set of multiple images from the database based on the processing; identifying subsets of images among the set of multiple images by analyzing metadata attribute values of the set of multiple images and nearest neighbor distance values between the query image and the multiple images in the set; determining one or more items of metadata attributable to the query image by processing metadata of the subsets of images; and outputting, to a user, the items of metadata in response to the query image.

Generating metadata for image-based querying

Methods, systems, and computer program products for generating search determinations for assortment planning and buying using visual sketches are provided herein. A computer-implemented method includes processing a query image by identifying one or more visual features in the query image and applying at least one nearest neighbor algorithm to the one or more identified visual features; identifying, from one or more databases, multiple images based at least in part on the processing; generating a result set by applying one or more smoothing algorithms to the multiple identified images; generating at least one sketch based at least in part on the result set; and outputting the at least one generated sketch to one or more users via a user interface.

Generating search determinations for assortment planning using visual sketches

A computer scans user data for upcoming events and identifies a potential gift giving event from these events using contextual information about the events to determine the events' nature. The computer identifies a plurality of event attendees and analyzes information about two or more of the event attendees. The computer groups a plurality of people into a group of potential gift collaborators, wherein the plurality of people is chosen from the plurality of attendees. The computer generates a first potential gift list. The computer analyzes gift giving abilities of the group members and selects, based partially upon the analysis, a gift giver. The computer generates a second potential gift list with detailed information about one or more potential gifts wherein the gifts are based partially on the first potential gift list. The computer provides at least a portion of the second potential gift list to the selected gift giver.

Computer provided assistance for coordinating gift collaboration

A neural network based search system is provided. A first digital image is analyzed by a user device. A targeted object in the first digital image is determined based, at least in part, on (i) the characteristics of the first digital image and (ii) the features of the targeted object. A vector array is generated based, at least in part, on (i) the first digital image and (ii) the targeted object. The vector array is analyzed by the user device. The targeted object is determined based, at least in part, by the vector array. A plurality of digital images is identified based, at least in part, on the similarity of the plurality of digital images and (i) the first digital image and (ii) the targeted object responsive to identifying a plurality of digital images, a query processing is generated. The query map is generated on a user device.

Visually guided query processing

A method comprising using at least one hardware processor for: receiving a topic under consideration (TUC) and a set of claims referring to the TUC; identifying semantic similarity relations between claims of the set of claims; clustering the claims into a plurality of claim clusters based on the identified semantic similarity relations, wherein said claim clusters represent semantically different claims of the set of claims; and generating a list of non-redundant claims comprising said semantically different claims.

Semantic merge of arguments

Methods, systems, and computer program products for pressure-based apparel image searching are provided herein. A computer-implemented method includes determining an object of interest within an image displayed on a screen by detecting physical contact imparted by a user at a particular position on the screen corresponding to where the first object is located; quantifying the amount of pressure applied by the user via the physical contact; determining additional objects of interest based on the amount of pressure applied by the user by identifying objects that are located within a specified distance from the first object, and wherein the specified distance corresponds to the amount of pressure applied by the user; retrieving, from at least one electronic commerce website, images of products corresponding to the first object of interest and images of products corresponding to each of the additional objects of interest; and displaying the retrieved images on the screen.

A method comprising using at least one hardware processor for: identifying relations between pairs of claims of a set of claims; aggregating the claims of the set of claims into a plurality of clusters based on the identified relations; generating a plurality of arguments from the plurality of clusters, wherein each of the arguments is generated from a cluster of the plurality of clusters, and wherein each of the arguments comprises at least one claim of the set of claims, scoring each possible set of a predefined number of arguments of the plurality of arguments, based on a quality of each argument of the predefined number of arguments and on diversity between the predefined number of arguments; and generating a speech, wherein the speech comprises a top scoring possible set of the possible set of the predefined number of arguments.

Automatic generation of a speech by processing raw claims to a set of arguments

Disclosed herein is a framework for facilitating adaptive anatomical region prediction. In accordance with one aspect, a set of exemplar images including annotated first landmarks is received. User definitions of first anatomical regions in the exemplar images are obtained. The framework may detect second landmarks in a subject image. It may further compute anatomical similarity scores between the subject image and the exemplar images based on the first and second landmarks, and predict a second anatomical region in the subject image by adaptively combining the first anatomical regions based on the anatomical similarity scores.

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Bengaluru, India

Vikas Raykar