Patents by Inventor Pierre L'Host
Pierre L'Host has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 12338191Abstract: The present invention relates to the field of crop fertilization, and particularly to a biofertilizing bacterial strain. In particular, the present invention relates to the bacterial strain deposited on Oct. 24, 2018, at the Collection Nationale de Culture de Microorganismes (CNCM), 28 rue du Dr. Roux, 75724 PARIS CEDEX 15, under the Budapest Treaty under number CNCM I-5373, and to the uses of this strain. The invention also relates to a composition comprising the above-mentioned bacterial strain and to a fertilization process comprising the application of this composition to a plant or to a soil.Type: GrantFiled: December 19, 2019Date of Patent: June 24, 2025Assignees: Universite de Lorraine, Institut National de la Recherche AgronomiqueInventors: Sophie Slezack-Deschaumes, Séverine Piutti, Pierre L'Yvonnet, Sandro Roselli
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Publication number: 20250161887Abstract: A floating and energy-autonomous device (1) configured to dispense a solid compound (2) into a volume (3v) of liquid is provided. The device (1) has a reservoir (9) of solid compound (2), and a pump (13) configured to generate a circulating flow of the liquid (3) coming from the volume (3v) of liquid and passing through the reservoir (9) before returning to the volume (3v) of liquid. The device (1) includes a sensor (17) generates a sensor signal indicative of a parameter of the volume (3v) of liquid or of a gaseous environment (4) of the device (1), and/or an antenna (18) configured to receive a radio signal, and to generate an antenna signal. The device (1) includes a microcontroller (19) configured to command the pump (13) and to regulate the circulating flow rate of the liquid (3) on the basis of the sensor signal and/or of the antenna signal.Type: ApplicationFiled: January 20, 2023Publication date: May 22, 2025Applicant: IOTCOInventors: Pierre L'Hoest, Sébastien Dawans
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Patent number: 12275677Abstract: The present invention relates to the field of crop fertilization, and particularly to a biofertilizing bacterial strain. In particular, the present invention relates to the bacterial strain deposited on Oct. 24, 2018, at the Collection Nationale de Culture de Microorganismes (CNCM), 28 rue du Dr. Roux, 75724 PARIS CEDEX 15, under the Budapest Treaty under number CNCM I-5372, and to the uses of this strain. The invention also relates to a composition comprising the above-mentioned bacterial strain and to a fertilization process comprising the application of this composition to a plant or to a soil.Type: GrantFiled: December 19, 2019Date of Patent: April 15, 2025Assignees: Universite de Lorraine, Institut National de la Recherche AgronomiqueInventors: Sophie Slezack-Deschaumes, Séverine Piutti, Pierre L'Yvonnet, Sandro Roselli
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Patent number: 12276130Abstract: A floating and energy-autonomous device (1) configured to dispense a solid compound (2) into a volume (3v) of liquid. The device (1) includes a reservoir (9) of solid compound (2), and a pump (13) configured to generate a circulating flow of the liquid (3) coining from the volume (3v) of liquid and passing through the reservoir (9) before returning to the volume (3v) of liquid. The device (1) includes a sensor (17) configured to generate a sensor signal indicative of a parameter of the volume (3v) of liquid or of a gaseous environment (4) of the device (1), and/or an antenna (18) configured to receive a radio signal, and to generate an antenna signal. The device (1) includes a microcontroller (19) configured to command the pump (13) and to regulate the circulating flow rate of the liquid (3) on the basis of the sensor signal and/or of the antenna signal.Type: GrantFiled: February 8, 2022Date of Patent: April 15, 2025Assignee: IOTCOInventors: Pierre L'Hoest, Sébastien Dawans
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Patent number: 12254390Abstract: A method, system and apparatus of ensembling, including inputting a set of models that predict different sets of attributes, determining a source set of attributes and a target set of attributes using a barycenter with an optimal transport metric, and determining a consensus among the set of models whose predictions are defined on the source set of attributes.Type: GrantFiled: April 29, 2019Date of Patent: March 18, 2025Assignee: International Business Machines CorporationInventors: Youssef Mroueh, Pierre L. Dognin, Igor Melnyk, Jarret Ross, Tom Sercu, Cicero Nogueira Dos Santos
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Publication number: 20240135238Abstract: One or more systems, devices, computer program products and/or computer implemented methods of use provided herein relate to a process of mitigating biased training instances associated with a machine learning model without additional refitting of the machine learning model. A system can comprise a memory that stores computer executable components, and a processor that executed the computer executable components stored in the memory, wherein the computer executable components can comprise a training data influence estimation component and an influence mitigation component. The training data influence estimation component can receive a pre-trained machine learning model and calculate a fairness influence score of training instances on group fairness metrics associated with the pre-trained machine learning model.Type: ApplicationFiled: October 10, 2022Publication date: April 25, 2024Inventors: Prasanna Sattigeri, Soumya Ghosh, Inkit Padhi, Pierre L. Dognin, Kush Raj Varshney
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Publication number: 20240070404Abstract: Obtain access to a pretrained encoder-decoder language model. Using a dataset including a plurality of text-graph pairs, carry out first fine-tuning training on the pre-trained language model by minimizing cross-entropy loss. A text portion of each text-graph pair includes a list of text tokens and a graph portion of each text-graph pair includes a list of graph tokens. The first fine-tuning training results in an intermediate model. Carry out second fine-tuning training on the intermediate model, by reinforcement learning, to obtain a final model. Make the final model available for deployment.Type: ApplicationFiled: August 26, 2022Publication date: February 29, 2024Inventors: Pierre L. Dognin, Inkit Padhi, Igor Melnyk, Payel Das
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Publication number: 20240013066Abstract: A knowledge graph is constructed as part of a multi-stage process using pretrained language models. Input text in a natural language format is received. In a first stage, a plurality of nodes is generated using a pretrained language model, where the nodes correspond to entities of the input text. In the second stage edges to interconnect the plurality of nodes are generated. The edges are generated responsive to generating each of the plurality of nodes.Type: ApplicationFiled: July 8, 2022Publication date: January 11, 2024Inventors: Igor Melnyk, Pierre L. Dognin, Payel Das
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Patent number: 11829726Abstract: Systems, computer-implemented methods, and computer program products to facilitate a dual learning bridge between text and a knowledge graph are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise a model component that employs a model to learn associations between text data and a knowledge graph. The computer executable components further comprise a translation component that uses the model to bidirectionally translate second text data and one or more knowledge graph paths based on the associations.Type: GrantFiled: January 25, 2021Date of Patent: November 28, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Pierre L. Dognin, Igor Melnyk, Inkit Padhi, Payel Das
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Publication number: 20230360135Abstract: A system for facilitating freight transactions that includes a secure portal for receiving users’ (carriers, forwarders, shippers, and market makers) data that includes orders and capacity postings between destinations. The system also includes a back-end modules configured for collecting capacity/shipping volume data to generate forecast data, managing derivative contracts, determining best possible routing given the orders and capacity postings, breaking the best possible routing into component segments that is then traded as derivative contracts, providing report, managing settlement and clearinghouse functions, and receiving risk assessment about the forecast data. The system further includes an interface layer for facilitating communications between the portal and the back-end modules. The system moreover includes a contract and capacity management module configured for enabling the carriers and the forwarders to strategically position their capacity.Type: ApplicationFiled: March 14, 2023Publication date: November 9, 2023Inventors: Pierre L. Laurent, Petere Miner
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Publication number: 20230250660Abstract: A floating and energy-autonomous device (1) configured to dispense a solid compound (2) into a volume (3v) of liquid. The device (1) includes a reservoir (9) of solid compound (2), and a pump (13) configured to generate a circulating flow of the liquid (3) coming from the volume (3v) of liquid and passing through the reservoir (9) before returning to the volume (3v) of liquid. The device (1) includes a sensor (17) configured to generate a sensor signal indicative of a parameter of the volume (3v) of liquid or of a gaseous environment (4) of the device (1), and/or an antenna (18) configured to receive a radio signal, and to generate an antenna signal. The device (1) includes a microcontroller (19) configured to command the pump (13) and to regulate the circulating flow rate of the liquid (3) on the basis of the sensor signal and/or of the antenna signal.Type: ApplicationFiled: February 8, 2022Publication date: August 10, 2023Applicant: IOTCOInventors: Pierre L'Hoest, Sébastien Dawans
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Patent number: 11630989Abstract: A computing device receives a data X and Y, each having N samples. A function f(x,y) is defined to be a trainable neural network based on the data X and the data Y. A permuted version of the data Y is created. A loss mean is computed based on the trainable neural network f(x,y), the permuted version of the sample data Y, and a trainable scalar variable ?. A loss with respect to the scalar variable ? and the trainable neural network is minimized. Upon determining that the loss is at or below the predetermined threshold, estimating a mutual information (MI) between a test data XT and YT. If the estimated MI is above a predetermined threshold, the test data XT and YT is deemed to be dependent. Otherwise, it is deemed to be independent.Type: GrantFiled: March 9, 2020Date of Patent: April 18, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Youssef Mroueh, Pierre L. Dognin, Igor Melnyk, Jarret Ross, Tom D. J. Sercu
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Patent number: 11620711Abstract: A system for facilitating freight transactions that includes a secure portal for receiving users' (carriers, forwarders, shippers, and market makers) data that includes orders and capacity postings between destinations. The system also includes a back-end modules configured for collecting capacity/shipping volume data to generate forecast data, managing derivative contracts, determining best possible routing given the orders and capacity postings, breaking the best possible routing into component segments that is then traded as derivative contracts, providing report, managing settlement and clearinghouse functions, and receiving risk assessment about the forecast data. The system further includes an interface layer for facilitating communications between the portal and the back-end modules. The system moreover includes a contract and capacity management module configured for enabling the carriers and the forwarders to strategically position their capacity.Type: GrantFiled: September 29, 2020Date of Patent: April 4, 2023Assignee: Future Freight CorporationInventors: Pierre L. Laurent, Petere Miner
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Publication number: 20220237389Abstract: Systems, computer-implemented methods, and computer program products to facilitate a dual learning bridge between text and a knowledge graph are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise a model component that employs a model to learn associations between text data and a knowledge graph. The computer executable components further comprise a translation component that uses the model to bidirectionally translate second text data and one or more knowledge graph paths based on the associations.Type: ApplicationFiled: January 25, 2021Publication date: July 28, 2022Inventors: Pierre L. Dognin, Igor Melnyk, Inkit Padhi, Payel Das
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Patent number: 11299633Abstract: Stable aqueous leucoindigo solution comprising an aromatic amine in the form of aniline or aniline and N-methylaniline, wherein said leucoindigo is in the form of an alkali metal salt; wherein the concentration of the aromatic amine is below 40 ppm determined according to ISO 14362-1:2017(E); and wherein the concentration of the leucoindigo salt is in a concentration range of from 10 to 45% by weight based on the total weight of the solution, and wherein the stability of the solution is measured at a temperature of 23° C.; or wherein the concentration of the leucoindigo salt is in a concentration range of from 45 to 65% by weight based on the total weight of the solution, and wherein the stability of the solution is measured at a temperature of 60° C.Type: GrantFiled: August 10, 2018Date of Patent: April 12, 2022Assignee: Archroma IP GmbHInventors: Erwin Lucic, Jorg Hubner, David Hyett, Michele Catherine Christianne Janssen, Karin Hendrika Maria Bessembinder, Pierre L. Woestenborghs, Marinus Petrus Wilhelmus Maria Rijkers
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Publication number: 20220076130Abstract: Run a computerized numerical partial differential equation solver on at least one partial differential equation representing at least one physical constraint of a physical system, to generate a training data set. A true potential corresponds to an exact solution to the at least one partial differential equation. Using a computerized machine learning system, learn, from the training data set, a surrogate of a gradient of the true potential. Using the computerized machine learning system, apply Langevin sampling to the learned surrogate of the gradient, to obtain a plurality of samples corresponding to candidate designs for the physical system. Make the plurality of samples available to a fabrication entity.Type: ApplicationFiled: August 31, 2020Publication date: March 10, 2022Inventors: Thanh Van Nguyen, Youssef Mroueh, Samuel Chung Hoffman, Payel Das, Pierre L. Dognin, Giuseppe Romano, Chinmay Hegde
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Publication number: 20220064076Abstract: The present invention relates to the field of crop fertilization, and particularly to a biofertilizing bacterial strain. In particular, the present invention relates to the bacterial strain deposited on Oct. 24, 2018, at the Collection Nationale de Culture de Microorganismes (CNCM), 28 rue du Dr. Roux, 75724 PARIS CEDEX 15, under the Budapest Treaty under number CNCM I-5372, and to the uses of this strain. The invention also relates to a composition comprising the above-mentioned bacterial strain and to a fertilization process comprising the application of this composition to a plant or to a soil.Type: ApplicationFiled: December 19, 2019Publication date: March 3, 2022Applicants: Université de Lorraine, Institut National de la Recherche AgronomiqueInventors: Sophie SLEZACK - DESCHAUMES, Séverine PIUTTI, Pierre L'YVONNET, Sandro ROSELLI
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Publication number: 20220055960Abstract: The present invention relates to the field of crop fertilization, and particularly to a biofertilizing bacterial strain. In particular, the present invention relates to the bacterial strain deposited on Oct. 24, 2018, at the Collection Nationale de Culture de Microorganismes (CNCM), 28 rue du Dr. Roux, 75724 PARIS CEDEX 15, under the Budapest Treaty under number CNCM I-5373, and to the uses of this strain. The invention also relates to a composition comprising the above-mentioned bacterial strain and to a fertilization process comprising the application of this composition to a plant or to a soil.Type: ApplicationFiled: December 19, 2019Publication date: February 24, 2022Applicants: Université de Lorraine, Institut National de la Recherche AgronomiqueInventors: Sophie SLEZACK - DESCHAUMES, Séverine PIUTTI, Pierre L'YVONNET, Sandro ROSELLI
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Patent number: 11238195Abstract: FEA model representing a RSW setup is defined and received in a computer system. The FEA model contains multiple solid elements representing a pair of electrodes and two workpieces. Numerically-calculated heat-power distributions and structural behaviors of the workpieces are obtained by conducting a time-marching simulation using FEA model with a set of time-dependent electrode forces and corresponding set of time-dependent electrical current. An overlapped contact area and corresponding contact center between first and second element contact faces of each of the solid element pairs in contact are determined. Respective elemental coordinates of the contact center in the first and second element contact faces are calculated. Augmented terms for Joule heating effects are added to the overall stiffness matrix for obtaining Joule heat rate power at each contact center, which is then distributed to respective corner nodes of the first and second element contact faces according to respective elemental coordinates.Type: GrantFiled: April 23, 2019Date of Patent: February 1, 2022Inventors: Iñaki Çaldichoury, Pierre L'Eplattenier
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Publication number: 20210287099Abstract: A computing device receives a data X and Y, each having N samples. A function f(x,y) is defined to be a trainable neural network based on the data X and the data Y. A permuted version of the data Y is created. A loss mean is computed based on the trainable neural network f(x,y), the permuted version of the sample data Y, and a trainable scalar variable ?. A loss with respect to the scalar variable ? and the trainable neural network is minimized. Upon determining that the loss is at or below the predetermined threshold, estimating a mutual information (MI) between a test data XT and YT. If the estimated MI is above a predetermined threshold, the test data XT and YT is deemed to be dependent. Otherwise, it is deemed to be independent.Type: ApplicationFiled: March 9, 2020Publication date: September 16, 2021Inventors: Youssef Mroueh, Pierre L. Dognin, Igor Melnyk, Jarret Ross, Tom D. J. Sercu