| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_ee65cd551841d9ae6e629b58037a418a
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_cdb60bc732ddb01d2f942290845768c0
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_c3a3050aaff944f96fc261bc4455ca53
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_8d595ec481a2a081fed4aa2fd4ff52d8
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_44cf16163a03df449bf3d352056fc544 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C30B7-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C30B29-58 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/C30B7-00 |
| filingDate |
2003-09-22-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ed1f078736b69fe12b22edacc198b5f3
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_d44906f702937aca63399afbe55d6ccb
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_9ed64432f039ba268bcf514544e90795 |
| publicationDate |
2004-05-21-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
WO-2004027574-A9 |
| titleOfInvention |
Optimal crystallization parameter determination process |
| abstract |
A crystallization parameter optimization process includes self-learning programs (22) based on neural networks (21) or smart algorithms that divine optimal crystallization conditions (19). The Operation of these programs is ideally coupled with a high throughput automated crystallization experiment system (2). Through the use of successful as well as unsuccessful crystallization experimental samples, the programs are efficiently able to predict optimal crystallization variables after sampling only a modest fraction of all possible variable permutations. |
| priorityDate |
2002-09-20-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |