Autonomous Item Fabrication Utilizing a Trained Machine Learning Model

Abstract

An autonomous item generation system implements a trained machine learning model configured to output fabrication instructions for generating an item and metadata describing the item, automatically and independent of user input. Fabrication instructions output by the machine learning model are transmitted to a fabrication device for generating the item. The autonomous item generation system generates a listing for the item based on the metadata output by the machine learning model and publishes the listing to a virtual marketplace. Analytics data describing feedback for the item listing is used to generate training data for the machine learning model. The training data is input to the machine learning model, which causes the machine learning model to refine at least one control parameter according to a loss function that penalizes negative differences between predicted and observed feedback data for the item. The machine learning model with the refined parameter(s) is then used by the autonomous item generation system to generate fabrication instructions and metadata for an additional item.

What is claimed is: 
     
         1 . A method for autonomous item generation implemented by at least one computing device, the method comprising:
 displaying a user interface that includes a control for specifying a machine learning model to be used in autonomously generating an item and a control for specifying an audience to be considered in generating the item;   receiving input at the user interface that specifies at least one of the machine learning model to be used or the audience to be considered in generating the item;   updating the user interface to display a preview representation of the item responsive to receiving the input;   receiving additional input that modifies at least one of the machine learning model to be used or the audience to be considered in generating the item; and   modifying the preview representation of the item responsive to receiving the additional input.   
     
     
         2 . The method of  claim 1 , wherein the machine learning model comprises a generative adversarial network trained to generate printing instructions for a three-dimensional object, the item comprises the three-dimensional object, and the preview representation of the item comprises a digital rendering of the three-dimensional object. 
     
     
         3 . The method of  claim 1 , wherein the machine learning model comprises a generative adversarial network trained to generate fabrication instructions for fabricating an article of clothing, the item comprises the article of clothing, and the preview representation of the item comprises a digital rendering of the article of clothing 
     
     
         4 . The method of  claim 1 , wherein the machine learning model comprises a generative adversarial network trained to generate printing instructions for a two-dimensional piece of art, the item comprises the two-dimensional piece of art, and the preview representation of the item comprises a digital rendering of the piece of art. 
     
     
         5 . The method of  claim 1 , wherein modifying the preview representation of the item comprises changing descriptive information for the item without changing a visual appearance of the preview representation of the item. 
     
     
         6 . The method of  claim 1 , wherein modifying the preview representation of the item comprises changing a visual appearance of the preview representation of the item without changing descriptive information for the item. 
     
     
         7 . The method of  claim 1 , wherein receiving input at the user interface that specifies the audience to be considered in generating the item comprises receiving information describing one or more demographic characteristics of the audience to be considered in generating the item. 
     
     
         8 . A system comprising:
 one or more processors; and   a computer-readable storage medium storing instructions that are executable by the one or more processors to perform operations comprising:
 displaying a user interface that includes a control for specifying a machine learning model to be used in autonomously generating an item and a control for specifying an audience to be considered in generating the item; 
 receiving input at the user interface that specifies at least one of the machine learning model to be used or the audience to be considered in generating the item; 
 updating the user interface to display a preview representation of the item responsive to receiving the input; 
 receiving additional input that modifies at least one of the machine learning model to be used or the audience to be considered in generating the item; and 
 modifying the preview representation of the item responsive to receiving the additional input. 
   
     
     
         9 . The system of  claim 8 , wherein the machine learning model comprises a generative adversarial network trained to generate printing instructions for a three-dimensional object, the item comprises the three-dimensional object, and the preview representation of the item comprises a digital rendering of the three-dimensional object. 
     
     
         10 . The system of  claim 8 , wherein the machine learning model comprises a generative adversarial network trained to generate fabrication instructions for fabricating an article of clothing, the item comprises the article of clothing, and the preview representation of the item comprises a digital rendering of the article of clothing 
     
     
         11 . The system of  claim 8 , wherein the machine learning model comprises a generative adversarial network trained to generate printing instructions for a two-dimensional piece of art, the item comprises the two-dimensional piece of art, and the preview representation of the item comprises a digital rendering of the piece of art. 
     
     
         12 . The system of  claim 8 , wherein modifying the preview representation of the item comprises changing descriptive information for the item without changing a visual appearance of the preview representation of the item. 
     
     
         13 . The system of  claim 8 , wherein modifying the preview representation of the item comprises changing a visual appearance of the preview representation of the item without changing descriptive information for the item. 
     
     
         14 . The system of  claim 8 , wherein receiving input at the user interface that specifies the audience to be considered in generating the item comprises receiving information describing one or more demographic characteristics of the audience to be considered in generating the item. 
     
     
         15 . A computer-readable storage medium storing instructions that are executable by a processing device to perform operations comprising:
 displaying a user interface that includes a control for specifying a machine learning model to be used in autonomously generating an item and a control for specifying an audience to be considered in generating the item;   receiving input at the user interface that specifies at least one of the machine learning model to be used or the audience to be considered in generating the item;   updating the user interface to display a preview representation of the item responsive to receiving the input;   receiving additional input that modifies at least one of the machine learning model to be used or the audience to be considered in generating the item; and   modifying the preview representation of the item responsive to receiving the additional input.   
     
     
         16 . The computer-readable storage medium of  claim 15 , wherein the machine learning model comprises a generative adversarial network trained to generate printing instructions for a three-dimensional object, the item comprises the three-dimensional object, and the preview representation of the item comprises a digital rendering of the three-dimensional object. 
     
     
         17 . The computer-readable storage medium of  claim 15 , wherein the machine learning model comprises a generative adversarial network trained to generate fabrication instructions for fabricating an article of clothing, the item comprises the article of clothing, and the preview representation of the item comprises a digital rendering of the article of clothing. 
     
     
         18 . The computer-readable storage medium of  claim 15 , wherein the machine learning model comprises a generative adversarial network trained to generate printing instructions for a two-dimensional piece of art, the item comprises the two-dimensional piece of art, and the preview representation of the item comprises a digital rendering of the piece of art. 
     
     
         19 . The computer-readable storage medium of  claim 15 , wherein modifying the preview representation of the item comprises changing descriptive information for the item without changing a visual appearance of the preview representation of the item. 
     
     
         20 . The computer-readable storage medium of  claim 15 , wherein receiving input at the user interface that specifies the audience to be considered in generating the item comprises receiving information describing one or more demographic characteristics of the audience to be considered in generating the item.