Patent ID: 11972225
Assignee: nan
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform steps based on a plurality of networks for processing at least one input, wherein the plurality of networks comprising:
at least one first network for encoding input from a text corpus,
at least one second network for encoding input from an image,
a third network having an input layer for receiving input from the first and second network and an output layer for generating a claim set corresponding to the text corpus and the image; and
wherein said steps performed by the one or more computers comprising:
acquiring, said at least one input, wherein said at least one input is the image corresponding to a class of patent documents,
encoding the image input via the plurality of networks,
generating a set of vectors via the plurality of networks, wherein the set of vectors corresponds to a partial representation of the image derived from the plurality of networks,
decoding the set of vectors via the plurality of networks based on the text corpus encoded by the plurality of networks that corresponds to the class of patent documents, and
obtaining the claim set via the plurality of networks corresponding to the image; and, wherein the plurality of networks is trained by:

initializing said at least one first, second, or third network of the plurality of networks with weights of a generated value and an adaptive learning rate to an initial value,
storing an input layer pattern of images and an output layer pattern of claims,
processing the input layer pattern of images in the plurality of networks to obtain an output pattern of claims,
calculating an error between the output layer pattern of images and the output pattern of claims, and
updating the adaptive learning rate with respect to the calculated error until a final trained state is achieved, otherwise, repeating steps above for as many iterations as necessary to reach the final trained state.