http://rdf.ncbi.nlm.nih.gov/pubchem/patent/KR-20200091234-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_8d0fc2b70675ee19bd5fc464f5ae9061 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H10-60 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H20-60 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q30-0631 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q30-06 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H20-60 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H10-60 |
| filingDate | 2019-01-22-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_f31f80e31a03ae916aaff17e553012fd http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_38f9d6065cabe2923bdc4c2d09fe678b http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_b1304da9bda1dc14f62048dfb40bd5e3 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_f3238033a8a5da67b36556801a6dfe3b http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_aec30b33092eedf72f361ce818c67846 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_d982bfbc01f855fd108cb0ed7a8986f2 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_1785b8a39894624bf9634cfc346c80c1 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_c648570dee31591b120ef6b20a73f1d0 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_70196e5acd0ec9d3e5c321f5c0ad38a8 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_f109f6b7df5e61698ba727e13f0d685d |
| publicationDate | 2020-07-30-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | KR-20200091234-A |
| titleOfInvention | Personalized system for recommending food and beverage |
| abstract | The personalized food and beverage recommendation system according to an embodiment of the present invention includes a memory storing a personalized food and beverage recommendation program and a processor for executing the personalized food and beverage recommendation program. As the processor executes the personalized food and beverage recommendation program, a graph pattern indicating a user internal element, a user external element, a food and beverage internal element and a food and beverage external element generated based on the response to the questionnaire, is based on the response to the questionnaire The personalized food and beverage recommendation information is executed for each user by executing the personalized food and beverage recommendation learning model constructed based on the personalized food and beverage candidate information preferred by the extracted user. At this time, the user internal element indicates whether the user's cognitive ability and the personality tendency of the user are included as important evaluation elements, and the user external element indicates whether the user's social tendency and the user's cultural enjoyment tendency are included as important evaluation elements, and the food and beverage The internal factors indicate whether information on the taste of food and beverages, the flavor of food and beverages or the texture of food and beverages is included as an important evaluation factor, and the external factors of food and beverage are important evaluations of the brand image of food and beverage, packaging design of food and beverage, or information on the price of food and beverage It indicates whether it is included as an element. |
| isCitedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2023090338-A1 http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2022220342-A1 |
| priorityDate | 2019-01-22-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 29.