| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_067fa7a6d043b14968b8dca94aa94e37 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C11B9-00 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16C20-70
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16C20-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2148
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16C60-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-047
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-082
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N33-0027
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-094 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16C60-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16C20-70
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16C20-30 |
| filingDate |
2021-12-17-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_2a2b21970dc5343e5a6f762ae195fc5e
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_633156f56e2c75ed92217394838072d4
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_0e3c7a58405d8b8be5bd57d41a382ca3
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_1a83b5a86f8a9204e2d1345627d45b8d
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_8a7481a964c2ec4bd03b2f7608158733
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_0cf932c2916e39700a93c94aa1e3691b
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_915ab1b19af0371e98040268ed287b35 |
| publicationDate |
2022-06-30-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
WO-2022136180-A1 |
| titleOfInvention |
Computer-implemented methods for training a neural network device and corresponding methods for generating a fragrance or flavor compositions |
| abstract |
The computer-implemented method (100) 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, comprises the steps of: - providing (105) 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 comprising at least two distinct ingredients and - training (110) 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, comprising 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. |
| priorityDate |
2020-12-21-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |