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公开(公告)号：US20220392583A1
公开(公告)日：2022-12-08
申请号：US17835845
申请日：2022-06-08
申请人： Firmenich SA
摘要： The system (200) for training an ensemble neural network device configured to execute the steps of: providing (205) a set of exemplar data, comprising at least one set of inputs (220) and at least one set of outputs (225) associated to the set of inputs, to a neural network device comprising an ensemble (230) of neural network devices, configured to provide independent predictions based upon the exemplar data, operating (210) the neural network device based upon the set of exemplar data, obtaining (215) the trained neural network device configured to provide an output, the neural network device further comprising at least two independent activation functions, whereof at least two of the independent activation functions are representative of the statistical distribution of the plurality of independent predictions, the neural network device being configured to provide at least one output (235, 236) for at least two said independent activation functions and the step of operating further comprising a step of operating each neural network device of the ensemble to provide an ensemble of outputs, the neural network device being trained to minimize the value representative of at least two said independent activation functions.
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2.发明公开公开(公告)号：US20230410950A1
公开(公告)日：2023-12-21
申请号：US18247717
申请日：2021-10-26
申请人： Firmenich SA
发明人： Guillaume GODIN , Ruud VAN DEURSEN
摘要： The chemical reaction encoding method (100) for one-step, multi-step and equilibrium reactions, comprises:—a step (105) of receiving, upon a computer interface, a chemical reaction graph comprising at least one chemical reaction reagent and at least one chemical reaction product, —a first step (110) of encoding, by a computing device, said chemical reaction graph describing the structure of at least one said reagent and said product, —a step (115) of determination, by a computing device, of changing bonds within the encoding representative of the chemical structures of at least one said reagent and said product, —a second step (120) of encoding, by a computing device, in a single string of characters, for at least one changing bond determined, at least one character representative of an atom subject to the change of bond, at least one character representative of the type of changing bond determined and at least one character representative of an atom resulting from the change of bond, in which a changing bond is encoded by a set of two characters representative of the changing bond determined, the first character being representative of the reagent bond and the second character being representative of the product bond, each character being selected in a library of bijective characters wherein one character is representative of one changing bond type and—a step (125) of providing, upon a computer interface, the string of characters corresponding to the encoding of changing bonds of the chemical reaction.
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3.发明公开公开(公告)号：US20230170062A1
公开(公告)日：2023-06-01
申请号：US17997362
申请日：2021-04-30
申请人： Firmenich SA
发明人： Christine VUILLEUMIER , Laurence AYMARD , Jean-Yves DE SAINT LAUMER , Sanja FITZGERALD , Guillaume GODIN
CPC分类号： G16C60/00 , G16C20/30 , G16C20/90 , G01N33/0062
摘要： The invention relates to a volatile liquid chemical compound physical parameter database construction method; a fragrance physical composition evolution prediction method and corresponding systems.
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4.发明公开公开(公告)号：US20240274243A1
公开(公告)日：2024-08-15
申请号：US18534565
申请日：2023-12-08
申请人： Firmenich SA
摘要： The method (100) to predict physico-chemical and/or odor properties value for chemical structures or compositions comprises the steps of:
defining (105) a representation of a chemical structure or composition,
executing (110) upon the representation defined, an end-to-end trained ensemble neural network or multi-branch neural network model to predict a physico-chemical and/or odor property value,
providing (115) the physico-chemical and/or odor property value,
the method further comprising:
providing (120) exemplar data to an end-to-end ensemble neural network or multi-branch neural network device comprising:
several neural network sub-devices configured to independent predictions,
a layer to output at least one value of the distribution of independent predictions and
said layer comprising a sampling device configured to output random values,
operating (125) the end-to-end ensemble neural network or multi-branch neural network device and
obtaining (130) the trained ensemble neural network or multi-branch neural network model.-
公开(公告)号：US20220196620A1
公开(公告)日：2022-06-23
申请号：US17554332
申请日：2021-12-17
申请人： FIRMENICH SA
发明人： Guillaume GODIN , Ruud VAN DEURSEN
摘要： A computer-implemented method for training an autoencoder neural network or generative adversarial network device to generate indeterministic and realistic digital representations of new fragrance or flavor ingredient compositions to be compounded, the method including the steps of: providing an original set of exemplar fragrance or flavor composition digital identifiers, said exemplar fragrance or flavor composition digital identifiers being representative of materialized fragrance or flavor compositions including at least so two distinct ingredients and training an autoencoder device or generative adversarial network device using the original set of exemplar fragrance or flavor composition digital identifiers to generate a fragrance or flavor composition generative model trained to generate new fragrance or flavor ingredient compositions, including at least two distinct ingredients, to be compounded. The trained autoencoder device or generative adversarial network device can be used to generate new fragrance or flavor ingredient compositions.
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