| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_e757fd4fedc4fe825bb81b1b466a0947 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F16-24578
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F16-35
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F16-285
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F16-2465
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N5-046
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F16-93 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F16-28
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F16-2458
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F16-2457
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F16-93 |
| filingDate |
2019-01-04-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate |
2021-07-20-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_1399478771e5063edc2206d7b0b270f1
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_6bb3b32f3dfd0fd815167c39870b076f
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4d72c542d7d78c40475eef7cdeaf1b7c
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_c75bcc33f0c5148dcb2a9191c7cff998
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_1a4c43aa3674e38ddda8f62914324aab
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_8740aa4d530236dbf039a89801138afb
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_c609e3045d4ce724c218e2ee9d23f2e5
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_8bada4c8c25a2a26e3f48a0523d76545 |
| publicationDate |
2021-07-20-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-11068490-B2 |
| titleOfInvention |
Automated document filtration with machine learning of annotations for document searching and access |
| abstract |
Computer-based methods, systems, and computer readable media for managing documents within a content repository or documents within the document subsets are provided. Documents within the content repository may be classified into one of a functional category and a clinical category. Documents are applied to a machine learning annotation and analysis module to automatically annotate the documents to indicate relationships between entities. A request is processed for the documents including one or more search terms, wherein the search terms pertain to one or more entities from a group of gene, gene variant, drug, cancer and a biomedical/clinical term. Documents satisfying the request are identified by comparing the one or more search terms to the annotations and specific sections of the documents, and determining a relevance of a document based on the comparison and a frequency of the one or more search terms in each of the specific sections. The identified documents are ranked according to custom techniques. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11663482-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11424012-B1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11151183-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11709877-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2021225466-A1 |
| priorityDate |
2019-01-04-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |