International Business Machines Corporation

Nuance Communications, Inc.

Shantanu Ravindra Godbole

Ashish Verma

Sachindra Joshi

Indrajit Bhattacharya

Jeffrey Mark Achtermann

Kevin W English

Ashwin Srinivasan

Smitkumar Narotambhai Marvaniya

Srujana Merugu

Ranjini Bangalore Guruprasad

Prasanta Kumar Ghosh

Jagabondhu Hazra

Manikandan Padmanaban

Jitendra Ajmera

Kedar Kulkarni

Jitendra Singh

Komminist Weldemariam

Ayush Jain

Himabindu Lakkaraju

Bernard Andrew Roden

Akshit Sharma

Komminist S Weldemariam

Mukesh Kumar Mohania

Ajay Gupta

Karthik Sankaranarayanan

Alberto Giammaria

Umamaheswari C Devi

Tanveer Afzal Faruquie

Vikas Raykar

Karthikeyan Ponnalagu

Ullas Balan Nambiar

Geeth Ranmal De Mel

Sumanta Mukherjee

Andrew Kinai

Navin Twarakavi

Kamal Das

Fred Ochieng Otieno

Richard J Tomsett

Kumar Avinava Dubey

A Scope 3 emission system includes a controller with a microprocessor and a memory. The memory stores executable instructions that when executed by the microprocessor cause the microprocessor to perform a computer-executable method. The method comprises generating embeddings based on enterprise financial transaction data, enterprise metadata, and crowd source data, and generating sector wise carbon-aware spatio-temporal weights indicative of an estimated level of Scope 3 emissions produced by a corresponding commodity sector. The method further includes generating a carbon-aware natural language processing (NLP) foundation model (FM) based on the embeddings and the sector wise carbon-aware spatio-temporal weights. User-generated data indicating at least one target commodity sector and spend data associated with the target commodity sector is input into the NLP FM, and the NLP FM outputs an estimation of the Scope 3 emissions based on the least one target commodity sector and the spend data.

Carbon footprint estimation using foundation 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

A computer implemented method identifies a set of environmental projects. A number of processor units identifies a group in a plurality of groups for organization of interest using organization parameters for the organization of interest. The number of processor units determines environmental performance for the organizations in the group using environmental data and the organization parameters for the organizations in the group. The number of processor units identifies the set of environmental projects for the organization of interest based on the environmental performance determined for the organizations in the group.

Net zero acceleration for organizations

A system, computer program product, and method are presented for forecasting a spatio-temporal calendar including predicted regions of interest based on time dependent factors such as long-term weather predictions, time-independent factors, and travel constraints. The method includes collecting information and constraints with respect to service visits. At least a portion of the collected information and constraints are directed toward weather and climate. The method also includes predicting weather and climate impacts on at least one geographical region of interest. The method further includes predicting, subject to the predictions of weather and climate impacts, one or more locations of interest within the at least one geographical region of interest that would be impacted by one or more service visits. The method also includes generating one or more spatio-temporal calendars that include the one or more locations of interest scheduled for the one or more service visits.

Spatio-temporal calendar generation

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

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

Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises identifying a plurality of code datasets prior to a data migration; analyzing the identified code datasets for a plurality of parameters; dynamically predicting a carbon footprint associated with the analyzed code datasets based on the plurality of parameters for each analyzed code dataset; and automatically optimizing the analyzed code datasets based on the predicted carbon footprint for data migration.

Carbon-aware code optimization

One embodiment provides a method, including: obtaining information related to farming activities of a farmer; predicting an annotation category for the information, wherein the annotation category identifies a topic of the information; selecting an annotator for annotating the information based upon the annotation category, wherein the selecting comprises utilizing (i) a social proximity constraint identifying a social connection between the farmer and another farmer and (ii) a farm signature constraint identifying a similarity of the farmer to another farmer; assigning the annotator to annotate the obtained information; and receiving annotations for the information.

Farm data annotation and bias detection

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

A computer generates labels for machine learning algorithms by retrieving, from a data storage circuit, multiple label sets that contain labels that each classify data points in a corpus of data. A graph is generated that includes a plurality of edges, each edge between two respective labels from different label sets of the multiple label sets. Weights are determined for the plurality of edges based upon a consistency between data points classified by two labels connected by the edges. An algorithm is applied that groups labels from the multiple label sets based upon the weights for the plurality of edges. Data points are identified from the corpus of data that represent conflicts within the grouped labels. An electronic message is transmitted in order to present the identified data points to entities for further classification. A new label set is generated using the further classification received from the entities.

Labeling of data for machine learning

A method and system. Target clusterability is calculated as an average of a respective clusterability of at least one target data item comprised by a target domain. Target-side matchability is calculated as an average of a respective matchability of each target centroid of the target domain to source centroids of a source domain, wherein the source domain comprises at least one source data item. Source-side matchability is calculated as an average of a respective matchability of each source centroid of said source centroids to the target centroids. Source-target pair matchability is calculated as an average of the target-side matchability and the source-side matchability. Cross-domain clusterability between the target domain and the source domain is calculated as a linear combination of the calculated target clusterability and the calculated source-target pair matchability. The cross-domain clusterability is transferred to a device.

Cross-domain clusterability evaluation for cross-guided data clustering based on alignment between data domains

Methods and arrangements for developing knowledge bases from social media. A question is obtained from social media. Social media are consulted, and a legitimacy of the question is ascertained. All the answers to the question are harvested from the social media including the rich media that is associated with these answers, and the question is filtered out if determined not to be legitimate.

Enhancing knowledge bases using rich social media

Methods, systems, and computer program products for repeat execution of RCA logic through run-time discovered topology pattern maps are provided herein. A computer-implemented method includes determining one or more topology paths of configuration items from a collection of multiple views of a target system; identifying one or more patterns from the one or more topology paths of configuration items; comparing the one or more identified patterns against a collection of data comprising mappings between (i) multiple root cause analysis logical expressions and (ii) multiple system architecture patterns, thereby identifying one or more of the multiple root cause analysis logical expressions to be executed on the one or more topology paths of configuration items; and generating a user-accessible link via a uniform resource locator for direct invocation of the one or more identified multiple root cause analysis logical expressions executed on the one or more topology paths of configuration items.

Repeat execution of root cause analysis logic through run-time discovered topology pattern maps

Growing community of inventors

New Delhi, India

Shantanu Ravindra Godbole