| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_b0e5f2d2b7b20ecee430e8ac22a95c4f |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10056
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30024
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2015-1488
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2015-1006 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-695
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-698
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6257
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N15-1475
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2148
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N15-1429
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-69
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-82 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V20-69
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00 |
| filingDate |
2019-03-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate |
2022-12-20-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_f26f7a00c66e21778fef63698893587e
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_06f4f9405d02f8107d5c3c02be6b0b68
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_fcd68a180912331a17a967db25da81f7
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_acba47155e37d5b6e87f86e05fa0e948 |
| publicationDate |
2022-12-20-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-11531844-B2 |
| titleOfInvention |
Using machine learning and/or neural networks to validate stem cells and their derivatives (2-D cells and 3-D tissues) for use in cell therapy and tissue engineered products |
| abstract |
A method is provided for non-invasively predicting characteristics of one or more cells and cell derivatives. The method includes training a machine learning model using at least one of a plurality of training cell images representing a plurality of cells and data identifying characteristics for the plurality of cells. The method further includes receiving at least one test cell image representing at least one test cell being evaluated, the at least one test cell image being acquired non-invasively and based on absorbance as an absolute measure of light, and providing the at least one test cell image to the trained machine learning model. Using machine learning based on the trained machine learning model, characteristics of the at least one test cell are predicted. The method further includes generating, by the trained machine learning model, release criteria for clinical preparations of cells based on the predicted characteristics of the at least one test cell. |
| priorityDate |
2018-03-16-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |