| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_11d063afd327c2473738f887902b6576 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-049
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N5-01
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-213
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N7-01
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q40-06
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2185
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-044
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-10
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6232
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6264 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q40-06
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-20 |
| filingDate |
2020-08-14-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate |
2023-01-17-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_b8c532010c5473606ee0731e239af691
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_05739309c282afc747f137809a24afd9
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_2447b538f4dfe24eca945d7b15283d09
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_f9282d92ce227b513bca88e4fbfedd69 |
| publicationDate |
2023-01-17-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-11556992-B2 |
| titleOfInvention |
System and method for machine learning architecture for enterprise capitalization |
| abstract |
Systems and methods are described in relation to specific technical improvements adapted for machine learning architectures that conduct classification on numerical and/or unstructured data. In an embodiment, two neural networks are utilized in concert to generate output data sets representative of predicted future states of an entity. A second learning architecture is trained to cluster prior entities based on characteristics converted into the form of features and event occurrence such that a boundary function can be established between the clusters to form a decision boundary between decision regions. These outputs are mapped to a space defined by the boundary function, such that the mapping can be used to determine whether a future state event is likely to occur at a particular time in the future. |
| priorityDate |
2019-08-14-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |