One-to-Many Automatic Content Generation
Techniques are disclosed for automatically generating new content using a trained 1-to-N generative adversarial network (GAN) model. In disclosed techniques, a computer system receives, from a computing device, a request for newly-generated content, where the request includes current content. The co...
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| creator | Woodward, David James Ross, Alan Martin Jain, Aashish Lundin, Jessica Rohde, Sönke Sollami, Michael Lonsdorf, Brian J Schoppe, Owen Winne |
| description | Techniques are disclosed for automatically generating new content using a trained 1-to-N generative adversarial network (GAN) model. In disclosed techniques, a computer system receives, from a computing device, a request for newly-generated content, where the request includes current content. The computer system automatically generates, using the trained 1-to-N GAN model, N different versions of new content, where a given version of new content is automatically generated based on the current content and one of N different style codes, where the value of N is at least two. After generating the N different versions of new content, the computer system transmits them to the computing device. The disclosed techniques may advantageously automate a content generation process, thereby saving time and computing resources via execution of the 1-to-N GAN machine learning model. |
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In disclosed techniques, a computer system receives, from a computing device, a request for newly-generated content, where the request includes current content. The computer system automatically generates, using the trained 1-to-N GAN model, N different versions of new content, where a given version of new content is automatically generated based on the current content and one of N different style codes, where the value of N is at least two. After generating the N different versions of new content, the computer system transmits them to the computing device. 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The disclosed techniques may advantageously automate a content generation process, thereby saving time and computing resources via execution of the 1-to-N GAN machine learning model.</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>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZNDwz0vVLcnX9U3Mq1RwLC3Jz00syUxWcM7PK0nNK1FwT81LLQKK5OfxMLCmJeYUp_JCaW4GZTfXEGcP3dSC_PjU4oLEZKDSkvjQYCMDI2NDI0sTY0NHQ2PiVAEAQqko2w</recordid><startdate>20230427</startdate><enddate>20230427</enddate><creator>Woodward, David James</creator><creator>Ross, Alan Martin</creator><creator>Jain, Aashish</creator><creator>Lundin, Jessica</creator><creator>Rohde, Sönke</creator><creator>Sollami, Michael</creator><creator>Lonsdorf, Brian J</creator><creator>Schoppe, Owen Winne</creator><scope>EVB</scope></search><sort><creationdate>20230427</creationdate><title>One-to-Many Automatic Content Generation</title><author>Woodward, David James ; Ross, Alan Martin ; Jain, Aashish ; Lundin, Jessica ; Rohde, Sönke ; Sollami, Michael ; Lonsdorf, Brian J ; Schoppe, Owen Winne</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2023129431A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2023</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>Woodward, David James</creatorcontrib><creatorcontrib>Ross, Alan Martin</creatorcontrib><creatorcontrib>Jain, Aashish</creatorcontrib><creatorcontrib>Lundin, Jessica</creatorcontrib><creatorcontrib>Rohde, Sönke</creatorcontrib><creatorcontrib>Sollami, Michael</creatorcontrib><creatorcontrib>Lonsdorf, Brian J</creatorcontrib><creatorcontrib>Schoppe, Owen Winne</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Woodward, David James</au><au>Ross, Alan Martin</au><au>Jain, Aashish</au><au>Lundin, Jessica</au><au>Rohde, Sönke</au><au>Sollami, Michael</au><au>Lonsdorf, Brian J</au><au>Schoppe, Owen Winne</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>One-to-Many Automatic Content Generation</title><date>2023-04-27</date><risdate>2023</risdate><abstract>Techniques are disclosed for automatically generating new content using a trained 1-to-N generative adversarial network (GAN) model. In disclosed techniques, a computer system receives, from a computing device, a request for newly-generated content, where the request includes current content. The computer system automatically generates, using the trained 1-to-N GAN model, N different versions of new content, where a given version of new content is automatically generated based on the current content and one of N different style codes, where the value of N is at least two. After generating the N different versions of new content, the computer system transmits them to the computing device. The disclosed techniques may advantageously automate a content generation process, thereby saving time and computing resources via execution of the 1-to-N GAN machine learning model.</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 | One-to-Many Automatic Content Generation |
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