| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_7275c55c1e8c289174d287cd2b51775e |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V2201-033
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30036
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61C2007-004
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20076
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61C7-002 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-579
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-82
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-764
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-24143
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61C13-0004
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-653
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N5-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61C7-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-764
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F19-10
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F19-12 |
| filingDate |
2017-07-26-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_7551299abea4eab4b083ef02f74af032
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_10c4acc095c6cdf6a65e3dd89e728ec9 |
| publicationDate |
2018-02-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
WO-2018022752-A1 |
| titleOfInvention |
Dental cad automation using deep learning |
| abstract |
A computer-implemented method of recognizing dental information associated with a dental model of dentition includes training a deep neural network to map a plurality of training dental models representing at least a portion of each one of a plurality of patients' dentitions to a probability vector including probability of the at least a portion of the dentition belonging to each one of a set of multiple categories. The category of the at least a portion of the dentition represented by the training dental model corresponds to the highest probability in the probability vector. The method includes receiving a dental model representing at least a portion of a patient's dentition and recognizing dental information associated with the dental model by applying the trained deep neural network to determine a category of the at least a portion of the patient's dentition represented by the received dental model. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-110009579-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111276239-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2018158411-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/EP-3824844-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111709959-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111709959-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11534275-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111276239-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-110009579-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/EP-4113373-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2023274622-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2021211871-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-109766877-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2020133180-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2020023811-A1 |
| priorityDate |
2016-07-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |