View source for 18540390. Interface for Patient-Provider Conversation and Auto-generation of Note or Summary simplified abstract (GOOGLE LLC)

=Interface for Patient-Provider Conversation   and Auto-generation of Note or Summary=


==Organization Name==

[[:Category:GOOGLE LLC|GOOGLE LLC]]


[[Category:GOOGLE LLC]]

==Inventor(s)==

[[:Category:Melissa Strader of San Jose CA (US)|Melissa Strader of San Jose CA (US)]][[Category:Melissa Strader of San Jose CA (US)]]

[[:Category:William Ito of Mountain View CA (US)|William Ito of Mountain View CA (US)]][[Category:William Ito of Mountain View CA (US)]]

[[:Category:Christopher Co of Saratoga CA (US)|Christopher Co of Saratoga CA (US)]][[Category:Christopher Co of Saratoga CA (US)]]

[[:Category:Katherine Chou of Palo Alto CA (US)|Katherine Chou of Palo Alto CA (US)]][[Category:Katherine Chou of Palo Alto CA (US)]]

[[:Category:Alvin Rajkomar of San Jose CA (US)|Alvin Rajkomar of San Jose CA (US)]][[Category:Alvin Rajkomar of San Jose CA (US)]]

[[:Category:Rebecca Rolfe of Menlo Park CA (US)|Rebecca Rolfe of Menlo Park CA (US)]][[Category:Rebecca Rolfe of Menlo Park CA (US)]]

==Interface for Patient-Provider Conversation   and Auto-generation of Note or Summary - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18540390 titled 'Interface for Patient-Provider Conversation   and Auto-generation of Note or Summary

==Simplified Explanation==

The computer-implemented method described in the abstract involves using a neural network for text-to-image generation to create image renditions of scenes based on textual descriptions. The neural network is trained to ensure that image renditions associated with the same textual description attract each other, while renditions associated with different descriptions repel each other. This is achieved by considering mutual information between corresponding pairs of images and texts.

* Neural network used for text-to-image generation
* Training to make image renditions attract or repel based on textual descriptions
* Consideration of mutual information between image-text pairs

== Potential Applications ==
This technology could be applied in various fields such as:
* Virtual reality and augmented reality
* Content creation for media and entertainment industries
* E-commerce for generating product images based on descriptions

== Problems Solved ==
This technology addresses the following issues:
* Bridging the gap between textual descriptions and visual representations
* Automating the process of generating images from text
* Enhancing the accuracy and relevance of image renditions

== Benefits ==
The benefits of this technology include:
* Improved efficiency in creating visual content
* Enhanced user experience through more accurate image generation
* Potential for cost savings in content creation processes

== Potential Commercial Applications ==
The potential commercial applications of this technology are:
* Image generation software for designers and content creators
* E-commerce platforms looking to enhance product visualization
* Virtual reality and augmented reality developers seeking realistic scene generation tools

== Possible Prior Art ==
One possible prior art in this field is the use of generative adversarial networks (GANs) for image generation from textual descriptions.GANs have been used to generate images based on text inputs, but the specific training method described in this patent application, focusing on attracting and repelling image renditions based on mutual information, may be a novel approach.

=== What is the accuracy rate of the image renditions generated by the neural network in this method? ===

The abstract does not provide specific information about the accuracy rate of the image renditions generated by the neural network. Further details or results from testing would be needed to determine the accuracy of the method.

=== How does the neural network distinguish between image renditions associated with the same textual description and those associated with different descriptions? ===

The abstract mentions that the neural network is trained to cause image renditions associated with the same textual description to attract each other and those associated with different descriptions to repel each other based on mutual information. The specifics of how this training process works and how the network learns to differentiate between similar and dissimilar descriptions would need to be explored in more detail.


==Original Abstract Submitted==

A computer-implemented method includes receiving, by a computing device, a particular textual description of a scene. The method also includes applying a neural network for text-to-image generation to generate an output image rendition of the scene, the neural network having been trained to cause two image renditions associated with a same textual description to attract each other and two image renditions associated with different textual descriptions to repel each other based on mutual information between a plurality of corresponding pairs, wherein the plurality of corresponding pairs comprise an image-to-image pair and a text-to-image pair. The method further includes predicting the output image rendition of the scene.



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