| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_eb81a7d09cb71a05da2afde6db68c272
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_2a64fa292403d7a5369303af9995cf01 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H10-40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-4266
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-14517
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-4277
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B2560-0223
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-14532
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-14507
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7264
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/Y02A90-10
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-6832 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-4266
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-4277
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-1477
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-14532
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-14507
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7264
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H40-63 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-1477
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-145
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-20 |
| filingDate |
2021-11-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_873aff2dbba9717d314b953616c9c092
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_50419e35bad8c663df6d7e41ebd1a5c5 |
| publicationDate |
2022-05-26-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2022160265-A1 |
| titleOfInvention |
Machine-learning based biosensor system |
| abstract |
Electrical characteristics of an electrical signal generated by an affinity-based senor are detected, where the affinity-based sensor is configured to bind to a particular biomarker within a body fluid sample and generate the electrical signal based on binding to the particular biomarker. One or more biometric characteristics of a subject are further detected from one or more other sensors. A data set comprising data describing each of the electrical characteristics and each of the one or more biometric characteristics is provided as an input to a machine learning model, which generates an output based on the input that identifies an amount of the particular biomarker present in the body fluid sample based on the input. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2022022751-A1 |
| priorityDate |
2020-11-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |