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公开(公告)号：US10902352B2
公开(公告)日：2021-01-26
申请号：US16734570
申请日：2020-01-06
IPC分类号： G06N20/00 , G06F16/35 , G06F16/901 , G06N5/02 , G06F16/23 , G06F16/906
摘要： 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.
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公开(公告)号：US20150356459A1
公开(公告)日：2015-12-10
申请号：US14613553
申请日：2015-02-04
CPC分类号： G06N99/005 , G06F17/30705 , G06F17/30958 , G06N5/02
摘要： 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.
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公开(公告)号：US20240303574A1
公开(公告)日：2024-09-12
申请号：US18179789
申请日：2023-03-07
发明人： Ayush Jain , Jagabondhu Hazra , Manikandan Padmanaban , Ranjini Bangalore Guruprasad , Shantanu R. Godbole
IPC分类号： G06Q10/0637 , G06Q10/0639
CPC分类号： G06Q10/0637 , G06Q10/06393
摘要： 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.
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公开(公告)号：US11803375B2
公开(公告)日：2023-10-31
申请号：US17346388
申请日：2021-06-14
CPC分类号： G06F8/76 , G06F9/4856 , G06N20/00
摘要： 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.
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公开(公告)号：US20220300909A1
公开(公告)日：2022-09-22
申请号：US17203585
申请日：2021-03-16
发明人： Andrew Kinai , Navin Twarakavi , Fred Ochieng Otieno , Kamal Chandra Das , Shantanu R. Godbole , Komminist Weldemariam
摘要： 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.
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公开(公告)号：US20210374161A1
公开(公告)日：2021-12-02
申请号：US16884776
申请日：2020-05-27
IPC分类号： G06F16/28 , A01B79/00 , G06F16/29 , G06F16/2458
摘要： 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.
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公开(公告)号：US09754216B2
公开(公告)日：2017-09-05
申请号：US14613553
申请日：2015-02-04
CPC分类号： G06N99/005 , G06F17/30705 , G06F17/30958 , G06N5/02
摘要： 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.
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公开(公告)号：US11948176B2
公开(公告)日：2024-04-02
申请号：US16828132
申请日：2020-03-24
IPC分类号： G06Q30/0282 , G06Q30/0203 , G06Q50/02
CPC分类号： G06Q30/0282 , G06Q30/0203 , G06Q50/02
摘要： 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.
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公开(公告)号：US20230196289A1
公开(公告)日：2023-06-22
申请号：US17553747
申请日：2021-12-16
发明人： Smitkumar Narotambhai Marvaniya , Kedar Kulkarni , Ranjini Bangalore Guruprasad , Jitendra Singh , Komminist Weldemariam , Shantanu R. Godbole , Chandrasekhar Narayanaswami
CPC分类号： G06Q10/101 , G06N20/00
摘要： A method and system generate news headlines from user input parameters. The user input parameters include a specified geographic region of interest and an industry of interest. Climate data and carbon emissions data for the specified geographic region of interest is retrieved. Supply chain dependencies are determined. A machine learning model is generated using the specified geographic region of interest, the industry, the climate data, the carbon emissions data, and the supply chain dependencies. The machine learning model performs an impact analysis on a supply chain based on the climate data and the carbon emissions data. The machine learning model predicts a supply chain performance for the industry based on the impact analysis. A news headline is automatically generated describing the predicted supply chain performance. The news headline includes an underlying basis for the predicted supply chain performance.
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公开(公告)号：US20230052540A1
公开(公告)日：2023-02-16
申请号：US17401548
申请日：2021-08-13
发明人： Richard J. Tomsett , Smitkumar Narotambhai Marvaniya , Geeth Ranmal de Mel , Jitendra Singh , NICOLAS ELIE GALICHET , Komminist Weldemariam , Shantanu R. Godbole
摘要： In an approach to jointly learning uncertainty-aware trend-informed neural network for a demand forecasting model, a machine learning model is trained to capture uncertainty in input forecasts. The uncertainty in a latent space is represented using an auto-encoder based neural architecture. The uncertainty-aware latent space is modeled and optimized to generate an embedding space. A time-series regressor model is learned from the embedding space. A machine learning model is trained for trend-aware demand forecasting based on said time-series regressor model.
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