# US 2025/0095337 A1 · Method for creating machine learning model for outputting feature map > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0095337 A1 - Document ID: US20250095337A1 - Status: Published application - Kind: A1 - Application number: 18/580,323 - Filed: 2022-07-19 - Published: 2025-03-20 - Earliest claimed: 2021-07-20 - Assignees: National Institute of Advanced Industrial Science and Technology - Applicants: National Institute of Advanced Industrial Science and Technology - Inventors: Junya FUKUOKA; Wataru UEGAMI - Web page: https://patentgrep.com/patent/US20250095337A1 ## CPC - G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting - A61B 6/03: Computed tomography [CT] - G01N 33/48: Biological material, e.g. blood, urine (G01N33/02, G01N33/26, G01N33/44, G01N33/46 take precedence); Haemocytometers (counting blood corpuscules distributed over a surface by scanning the surface G06M11/02) - G06T 7/00: Image analysis - G06T 7/0012: {Biomedical image inspection} - G06V 10/26: Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion - G06V 10/762: using clustering, e.g. of similar faces in social networks - G06V 10/764: using classification, e.g. of video objects - G06V 10/7715: {Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods} - G16H 30/40: for processing medical images, e.g. editing - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G06T 2207/10081: Computed x-ray tomography [CT] - G06T 2207/20081: Training; Learning - G06T 2207/30061: Lung - G06V 2201/03: Recognition of patterns in medical or anatomical images - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract This method for creating a machine learning model for outputting a feature map involves receiving a plurality of learning images, using an initial machine learning model to sort the plurality of learning images into respective initial clusters from among a plurality of initial clusters, resorting the plurality of initial clusters into a plurality of secondary clusters on the basis of the plurality of learning images as sorted into each of the plurality of initial clusters, and creating a m machine learning model by making the initial machine learning model learn the relationship between the plurality of initial clusters and the plurality of secondary clusters, the machine learning model being for sorting single inputted images into single secondary clusters from among the plurality of secondary clusters. ## Claims ### Claim at position 1 -26. (canceled) ### Claim 27 A method of creating a machine learning model comprising: receiving a plurality of data for learning; classifying each of the plurality of data for learning into a respective initial cluster of a plurality of initial clusters by using an initial machine learning model, the initial machine learning model being caused to at least learn to output a feature of an inputted datum from the datum; reclassifying the plurality of initial clusters into a plurality of secondary clusters based on the plurality of data for learning classified into the respective plurality of initial clusters; and creating a machine learning model by causing the initial machine learning model to learn a relationship between the plurality of initial clusters and the plurality of secondary clusters, the machine learning model classifying an inputted datum into one of the plurality of secondary clusters. ### Claim 28 (depends on 27) The method of claim 27, wherein the reclassifying comprises: presenting the plurality of data for learning classified into the respective plurality of initial clusters to a user; receiving a user input that associates each of the plurality of initial clusters to one of the plurality of secondary clusters; and reclassifying the plurality of initial clusters into a plurality of secondary clusters based on the user input. ### Claim 29 (depends on 28) The method of claim 28, wherein the plurality of secondary clusters are defined by the user. ### Claim 30 (depends on 27) The method of claim 27, wherein the plurality of secondary clusters are determined in accordance with a resolution of the plurality of data for learning. ### Claim 31 (depends on 27) The method of claim 27, wherein the plurality of data is a plurality of images. ### Claim 32 (depends on 31) The method of claim 31, wherein the plurality of images for learning comprise at least one of a plurality of partial images from fragmenting an image at a predefined resolution, an image for a pathological diagnosis, an image of tissue of a subject with interstitial pneumonia, an image of tissue of a subject without interstitial pneumonia, or a plurality of images of subjects with different diseases. ### Claim 33 (depends on 27) The method of claim 27 further comprising repeating the receiving, the classifying, and the reclassifying of datum within at least one secondary cluster among the plurality of secondary clusters as the plurality of data for learning. ### Claim 34 (depends on 27) The method of claim 27, wherein the created machine learning model is used for outputting a feature map. ### Claim 35 (depends on 27) A method of creating a machine learning model, comprising: receiving a plurality of data classified into at least one secondary cluster by a machine learning model created in accordance with the method of claim 27; classifying each of the plurality of received data into a respective initial cluster of a plurality of initial clusters by using an initial machine learning model, the initial machine learning model being caused to at least learn to output a feature of an inputted datum from the datum; reclassifying the plurality of initial clusters into a plurality of secondary clusters based on the plurality of received data classified into the respective plurality of initial clusters; and creating a machine learning model by causing the initial machine learning model to learn a relationship between the plurality of initial clusters and the plurality of secondary clusters, the machine learning model classifying an inputted datum into one of the plurality of secondary clusters. ### Claim 36 (depends on 31) A method of creating a feature map, comprising: receiving a target image; fragmenting the target image into a plurality of regional images; classifying each of the plurality of regional images into a respective secondary cluster of the plurality of secondary clusters by inputting the plurality of regional images into a machine learning model created by the method of claim 31; and creating a feature map by separating each of the plurality of regional images in the target image in accordance with respective classifications. ### Claim 37 (depends on 36) The method of claim 36, wherein the separating comprises coloring regional images belonging to the same classification among the plurality of regional images with the same color. ### Claim 38 (depends on 36) A method of estimating a state related to a disease of a subject, comprising: obtaining a feature map created in accordance with the method of claim 36, the target image being an image of tissue of the subject; and estimating a state related to a disease of the subject based on the feature map. ### Claim 39 (depends on 38) The method of claim 38, wherein the estimating the state comprises at least one of estimating a type of interstitial pneumonia of the subject or estimating whether the subject has usual interstitial pneumonia. ### Claim 40 (depends on 38) The method of claim 38, wherein the estimating a state related to a disease of the subject based on the created feature map comprises: calculating a frequency of each of the plurality of secondary clusters from the feature map; and estimating a state related to the disease based on the frequency. ### Claim 41 (depends on 38) The method of claim 38, wherein creating the feature map comprises creating a plurality of feature maps, the plurality of feature maps having different resolutions from one another. ### Claim 42 (depends on 41) The method of claim 41, wherein the estimating a state related to a disease based on the created feature map comprises: calculating a frequency of each of the plurality of secondary clusters from each of the plurality of feature maps; and estimating a state related to the disease based on the frequency. ### Claim 43 (depends on 41) The method of claim 41, wherein the estimating a state related to a disease based on the created feature map comprises: identifying an error in at least one of the plurality of feature maps by using the plurality of feature maps; and estimating a state related to the disease based on at least one feature map excluding the at least one feature map in which an error has been identified. ### Claim 44 (depends on 38) The method of claim 38, further comprising: analyzing survival time of the subject whose state related to the disease has been estimated based on the created feature map; and identifying at least one secondary cluster contributing to the estimated state among a plurality of secondary clusters in the feature map. ### Claim 45 A system for creating a machine learning model, comprising: receiving means for receiving a plurality of data for learning; classifying means for classifying each of the plurality of data for learning into a respective cluster of a plurality of initial clusters by using an initial machine learning model, the initial machine learning model being caused to at least learn to output a feature of an inputted datum from the datum; reclassifying means for reclassifying the plurality of initial clusters into a plurality of secondary clusters based on the plurality of data for learning classified into the respective plurality of initial clusters; and creating means for creating a machine learning model by causing the initial machine learning model to learn a relationship between the plurality of initial clusters and the plurality of secondary clusters, the machine learning model classifying an inputted datum into one of the plurality of secondary clusters. ### Claim 46 A computer readable storage medium for storing a program for creating a machine learning model, the program being executed in a computer system comprising a processing unit, the program causing the processing unit to execute processing comprising: receiving a plurality of data for learning; classifying each of the plurality of data for learning into a respective cluster of a plurality of initial clusters by using an initial machine learning model, the initial machine learning model being caused to at least learn to output a feature of an inputted datum from the datum; reclassifying the plurality of initial clusters into a plurality of secondary clusters based on the plurality of data for learning classified into the respective plurality of initial clusters; and creating a machine learning model by causing the initial machine learning model to learn a relationship between the plurality of initial clusters and the plurality of secondary clusters, the machine learning model classifying an inputted datum into one of the plurality of secondary clusters.