# US 12,119,103 B2 · GANs for latent space visualizations > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,119,103 B2 - Document ID: US12119103B2 - Status: Granted - Kind: B2 - Application number: 17/648,936 - Filed: 2022-01-25 - Published: 2022-10-06 - Granted: 2024-10-15 - Earliest claimed: 2021-03-30 - Assignees: Tempus AI, Inc. - Applicants: Tempus AI, Inc. - Inventors: Ryan Mork - Web page: https://patentgrep.com/patent/US12119103B2 ## CPC - G16H 30/40: for processing medical images, e.g. editing - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/047: Probabilistic or stochastic networks - G06N 3/0475: Generative networks - G06N 3/094: Adversarial learning - G06N 3/123: DNA computing - G06N 20/00: Machine learning - G06T 7/0012: {Biomedical image inspection} - G16B 25/10: Gene or protein expression profiling; Expression-ratio estimation or normalisation - G16B 40/20: Supervised data analysis - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G06T 2207/10024: Color image - G06T 2207/10056: Microscopic image - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro - G06T 2207/30096: Tumor; Lesion - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY ## Abstract The disclosure provides a method of analyzing a patient. The method includes receiving a plurality of latent space representations of patient data including genetic data associated with a plurality of patients, providing each of the plurality of latent space representations to a trained model, receiving a plurality of images from the trained model, each image included in the plurality of images being associated with a patient included in the plurality of patients, grouping at least a portion of the plurality of images into a plurality of groups, and displaying the plurality of groups to at least one user. ## Claims ### Claim 1 A method of analyzing a subject, the method comprising: receiving first genetic data comprising at least one of RNA data or DNA data associated with a subject; applying a machine learning model or a dimensionality reduction algorithm to the first genetic data to generate second genetic data having a lower dimensionality than the first genetic data; providing the second genetic data to a trained model; receiving a subject image generated by the trained model using the second genetic data; identifying a pattern in the subject image that is present in at least one other image in a set of images generated by the trained model; providing the subject image to an analysis engine; receiving subject information from the analysis engine; and causing the subject information to be output to at least one of a medical practitioner or a memory. ### Claim 2 (depends on 1) The method of claim 1, wherein the first genetic data comprises at least one thousand RNA expression levels, and wherein the second genetic data comprises no more than one hundred dimensions of values. ### Claim 3 (depends on 1) The method of claim 1, wherein applying a machine learning model or a dimensionality reduction algorithm to the first genetic data to generate second genetic data comprises: providing the first genetic data to a trained autoencoder; and receiving the second genetic data from the trained autoencoder. ### Claim 4 (depends on 1) The method of claim 1, wherein the trained model comprises a neural network. ### Claim 5 (depends on 4) The method of claim 4, wherein the neural network is a U-Net. ### Claim 6 (depends on 1) The method of claim 1, wherein the trained model comprises a generator comprising a neural network, the generator being previously trained by training a training model comprising the generator and a discriminator based on a set of latent space representations and a set of images. ### Claim 7 (depends on 1) The method of claim 1, wherein applying a machine learning model or a dimensionality reduction algorithm to the first genetic data to generate second genetic data comprises: applying one or more of principal component analysis (PCA) or t-distributed stochastic neighbor embedding to the first genetic data. ### Claim 8 (depends on 6) The method of claim 6, wherein each latent space representation included in the set of latent space representations is generated by a trained autoencoder, and wherein the second genetic data comprises a latent space representation generated by the trained autoencoder. ### Claim 9 (depends on 6) The method of claim 6, wherein the training the training model comprises: providing a latent space representation included in the set of latent space representations to the generator; receiving a generated image from the generator; providing the generated image and an image included in the set of images to the discriminator; and updating weights included in the generator and the discriminator based on the generated image and the image included in the set of images to the discriminator. ### Claim 10 (depends on 9) The method of claim 9, wherein the set of images comprises object images. ### Claim 11 (depends on 10) The method of claim 10, wherein the set of images comprises object images comprises images of at least one of an airplane, a bird, a car, a cat, a deer, a dog, a horse, a monkey, a ship, or a truck. ### Claim 12 (depends on 6) The method of claim 6, wherein the set of images comprises cell images. ### Claim 13 (depends on 12) The method of claim 12, wherein the cell images comprise slide images. ### Claim 14 (depends on 1) The method of claim 1, wherein the subject information comprises information from a cohort of patients subjects. ### Claim 15 (depends on 1) The method of claim 1, wherein the subject information comprises at least one potential treatment for the subject. ### Claim 16 (depends on 1) The method of claim 1, wherein the identifying the pattern in the subject image comprises clustering the subject image with a subset of the set of images generated by the trained model. ### Claim 17 (depends on 16) The method of claim 16, wherein the analysis engine is configured to identify at least one diagnostic metric between the subject image and a group of patients subjects associated with the subset of the set of images. ### Claim 18 (depends on 17) The method of claim 17, wherein the at least one diagnostic metric comprises at least one of a gene variant present in the subject and at least a portion of the group of patients subjects, a disease present in the subject and at least a portion of the group of patients subjects, a preexisting condition present in the subject and at least a portion of the group of patients subjects, a treatment provided to at least a portion of the group of patients subjects, or a trial participated in by at least a portion of the group of patients subjects. ### Claim 19 A system for analyzing a subject, the system comprising: at least one memory; and at least one processor coupled to the at least one memory, the system configured to cause the at least one processor to execute instructions stored in the at least one memory to: receive first genetic data comprising at least one of RNA data or DNA data associated with a subject; generate apply a machine learning model or a dimensionality reduction algorithm to the first genetic data to generate second genetic data having a lower dimensionality than the first genetic data; provide the second genetic data to a trained model; receive a subject image generated by the trained model using the second genetic data; identify a pattern in the subject image that is present in at least one other image in a set of images generated by the trained model; provide the subject image to an analysis engine; receive subject information from the analysis engine; and cause the subject information to be output to at least one of a medical practitioner or a memory. ### Claim 20 A computer program product for analyzing a subject, the computer program product comprising instructions stored on a non-transitory computer readable medium to cause at least one processor to: receive first genetic data comprising at least one of RNA data or DNA data associated with a subject; apply a machine learning model or a dimensionality reduction algorithm to the first genetic data to generate second genetic data having a lower dimensionality than the first genetic data; provide the second genetic data to a trained model; receive a subject image generated by the trained model using the second genetic data; identify a pattern in the subject image that is present in at least one other image in a set of images generated by the trained model; provide the subject image to an analysis engine; receive subject information from the analysis engine; and cause the subject information to be output to at least one of a medical practitioner or a memory.