NETWORK OPTIMIZATION
A system may receive a cluster prediction requirement. The system may determine a first node conglomerate by sorting a first dataset into a first plurality of nodes. The system may determine a plurality of attributes by sorting a second dataset associated with the cluster prediction requirement. The...
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| creator | PARIKH, Ashesh ARAD, Alon SACHDEV, Sharad ASWATHNARAYANA, Suresh MYHRER, Ragnar-Miguel RAO, Tejas INTRILIGATOR, Joshua HUSAIN, Afzal ALFIERI, Scott Andrew |
| description | A system may receive a cluster prediction requirement. The system may determine a first node conglomerate by sorting a first dataset into a first plurality of nodes. The system may determine a plurality of attributes by sorting a second dataset associated with the cluster prediction requirement. The system may determine a second node conglomerate for each of the plurality of attributes. A node confidence score may be assigned to each of the second plurality of nodes. The system may determine a node graph based on a comparison between the first node conglomerate and the second node conglomerate. The node graph may be iteratively modified based on a node optimization threshold value to generate a harmonized node graph. The node optimization threshold value may be based on a map confidence score allotted to the node graph. |
| format | Patent |
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The system may determine a first node conglomerate by sorting a first dataset into a first plurality of nodes. The system may determine a plurality of attributes by sorting a second dataset associated with the cluster prediction requirement. The system may determine a second node conglomerate for each of the plurality of attributes. A node confidence score may be assigned to each of the second plurality of nodes. The system may determine a node graph based on a comparison between the first node conglomerate and the second node conglomerate. The node graph may be iteratively modified based on a node optimization threshold value to generate a harmonized node graph. 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The node optimization threshold value may be based on a map confidence score allotted to the node graph.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2021</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZBDxcw0J9w_yVvAPCPH09YxyDPH09-NhYE1LzClO5YXS3AzKbq4hzh66qQX58anFBYnJqXmpJfGhwUYGRoYGZqYGxoaOhsbEqQIAE9kfyA</recordid><startdate>20210304</startdate><enddate>20210304</enddate><creator>PARIKH, Ashesh</creator><creator>ARAD, Alon</creator><creator>SACHDEV, Sharad</creator><creator>ASWATHNARAYANA, Suresh</creator><creator>MYHRER, Ragnar-Miguel</creator><creator>RAO, Tejas</creator><creator>INTRILIGATOR, Joshua</creator><creator>HUSAIN, Afzal</creator><creator>ALFIERI, Scott Andrew</creator><scope>EVB</scope></search><sort><creationdate>20210304</creationdate><title>NETWORK OPTIMIZATION</title><author>PARIKH, Ashesh ; ARAD, Alon ; SACHDEV, Sharad ; ASWATHNARAYANA, Suresh ; MYHRER, Ragnar-Miguel ; RAO, Tejas ; INTRILIGATOR, Joshua ; HUSAIN, Afzal ; ALFIERI, Scott Andrew</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2021065031A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2021</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>PARIKH, Ashesh</creatorcontrib><creatorcontrib>ARAD, Alon</creatorcontrib><creatorcontrib>SACHDEV, Sharad</creatorcontrib><creatorcontrib>ASWATHNARAYANA, Suresh</creatorcontrib><creatorcontrib>MYHRER, Ragnar-Miguel</creatorcontrib><creatorcontrib>RAO, Tejas</creatorcontrib><creatorcontrib>INTRILIGATOR, Joshua</creatorcontrib><creatorcontrib>HUSAIN, Afzal</creatorcontrib><creatorcontrib>ALFIERI, Scott Andrew</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>PARIKH, Ashesh</au><au>ARAD, Alon</au><au>SACHDEV, Sharad</au><au>ASWATHNARAYANA, Suresh</au><au>MYHRER, Ragnar-Miguel</au><au>RAO, Tejas</au><au>INTRILIGATOR, Joshua</au><au>HUSAIN, Afzal</au><au>ALFIERI, Scott Andrew</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>NETWORK OPTIMIZATION</title><date>2021-03-04</date><risdate>2021</risdate><abstract>A system may receive a cluster prediction requirement. The system may determine a first node conglomerate by sorting a first dataset into a first plurality of nodes. The system may determine a plurality of attributes by sorting a second dataset associated with the cluster prediction requirement. The system may determine a second node conglomerate for each of the plurality of attributes. A node confidence score may be assigned to each of the second plurality of nodes. The system may determine a node graph based on a comparison between the first node conglomerate and the second node conglomerate. The node graph may be iteratively modified based on a node optimization threshold value to generate a harmonized node graph. The node optimization threshold value may be based on a map confidence score allotted to the node graph.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
| title | NETWORK OPTIMIZATION |
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