# US 2026/0065477 A1 · Computer-implemented detection and processing of oral features > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0065477 A1 - Document ID: US20260065477A1 - Status: Published application - Kind: A1 - Application number: 19/386,551 - Filed: 2025-11-12 - Published: 2026-03-05 - Earliest claimed: 2020-06-24 - Applicants: ORAL TECH Al PTY LTD - Inventors: Padma Gadiyar; Praveen Narra; Anand Selvadurai; Radeeshwar Reddy; Sai Ainala; Hemadri Babu Jogi - Web page: https://patentgrep.com/patent/US20260065477A1 ## CPC - G06T 7/0012: {Biomedical image inspection} - A61B 5/0088: {for oral or dental tissue} - A61B 5/4547: {Evaluating teeth} - A61B 5/4552: {Evaluating soft tissue within the mouth, e.g. gums or tongue} - A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor} - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06N 3/0985: Hyperparameter optimisation; Meta-learning; Learning-to-learn - G06N 3/10: Interfaces, programming languages or software development kits, e.g. for simulating neural networks - G06T 7/0014: {using an image reference approach} - G16H 10/20: for electronic clinical trials or questionnaires - G16H 30/40: for processing medical images, e.g. editing - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/30: for calculating health indices; for individual health risk assessment - G16H 80/00: ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring - A61B 2576/02: specially adapted for a particular organ or body part - G06N 3/048: Activation functions - G06T 2207/10024: Color image - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30036: Dental; Teeth - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract Described herein are computer-implemented methods for analyzing an input image of a mouth region from a user to provide information regarding a disease or condition of the mouth region, a computing device configured to receive the input images from a user; and a trained machine learning system. In some embodiments, the computing device is configured to transmit an oral health score to the user. ## Claims ### Claim at position 1 -38. (canceled) ### Claim 39 A system for analyzing an input image of a mouth region to determine a disease or condition of the mouth region, the system comprising: a trained machine learning system comprising models that are trained using a dataset of training images, the dataset comprising one or both of: a dental caries feature and a periodontitis feature, the trained machine learning system further configured to: receive one or more images of the mouth region; extract image features from the one or more images; analyze the extracted image features generate a prediction based on a recognized feature within each of the one or more images; and generate an oral health score for each of the one or more images corresponding to the recognized feature associated with one or both of: the dental caries feature and the periodontitis feature. ### Claim 40 (depends on 39) The system of claim 39, wherein the dataset comprises images having one or more of: a resolution of about 32×32 to about 2048×2048; a greyscale; and a rectangular shape. ### Claim 41 (depends on 39) The system of claim 39, wherein the models are trained to recognize both the dental caries feature and the periodontitis feature from the one or more images. ### Claim 42 (depends on 39) The system of claim 39, wherein the trained machine learning system further processes the one or more of the images of the dataset to provide additional images for the dataset, and the processes performed on the one or more of the images comprise one or more of: adding noise, adjusting a contrast, adjusting a brightness, blurring, sharpening, flipping, rotating, adjusting a white balance, adjusting a color, or equivalents thereof. ### Claim 43 (depends on 39) The system of claim 39, further comprising: pre-processing the one or more images to extract the image features, wherein the pre-processing further comprises one or more of: adjusting a resolution of each of the one or more images; or converting each of the one or more images into a greyscale image. ### Claim 44 (depends on 41) The system of claim 41, wherein each of the trained models are stacked to provide the prediction. ### Claim 45 (depends on 39) The system of claim 39, further comprising a user interface configured for interaction with a user using one or both of: an application residing on a smartphone or a website associated with a computing device. ### Claim 46 (depends on 45) The system of claim 45, wherein the user interface is configured for the interaction with the user by providing visual aids to assist the user in capturing the one or more images of the mouth region. ### Claim 47 (depends on 46) The system of claim 46, wherein the visual aids include one or more of: frames, lines, points, geometric shapes, or combinations and equivalents thereof, in order to align, angle, or distance of an image sensor to different areas inside the mouth region. ### Claim 48 (depends on 39) The system of claim 39, wherein each of the one or more images is of a different area in the mouth region. ### Claim 49 (depends on 39) The system of claim 39, wherein the oral health score comprises a score for each individual tooth for each of the one or more images. ### Claim 50 (depends on 39) The system of claim 39, wherein the oral health score comprises a score for each gum region for each of the one or more images. ### Claim 51 (depends on 39) The system of claim 39, wherein the oral health score comprises a score for each individual tooth and gum region for each of the one or more images. ### Claim 52 (depends on 39) The system of claim 39, wherein a processor is configured to transmit the oral health score to a user. ### Claim 53 (depends on 39) The system of claim 39, wherein the oral health score comprises an indication of one or more of: dental caries, periodontitis, gingivitis, fillings, toothbrush abrasion, dental erosion, teeth sensitivity, oral cancer, cracked or broken teeth, mouth sores, halitosis, abscess, congenital tooth conditions, tongue disease, and one or more cosmetic conditions. ### Claim 54 A method for analyzing a mouth region to provide information regarding a disease or condition of the mouth region, the method comprising: at a trained machine learning system comprising models that are trained using a dataset of training images, wherein the dataset comprises one or both of: a dental caries feature and a periodontitis feature: receiving one or more images; extracting image features from the one or more images; analyzing the extracted image features to generate a prediction based on a recognized feature within each of the one or more images; and generating an oral health score for each of the one or more images corresponding to the recognized feature associated with one or both of the dental caries feature and the periodontitis feature. ### Claim 55 (depends on 54) The method of claim 54, further comprising transmitting, using a processor, the oral health score to a user. ### Claim 56 (depends on 54) The method of claim 54, wherein the oral health score comprises an indication of one or more of: dental caries, periodontitis, gingivitis, fillings, toothbrush abrasion, dental erosion, teeth sensitivity, oral cancer, cracked or broken teeth, mouth sores, halitosis, abscess, congenital tooth conditions, tongue disease, and one or more cosmetic conditions. ### Claim 57 A method for training a machine learning system for analyzing an input image to provide information regarding a disease or condition, the method comprising: receiving a dataset of training images, the dataset of training images comprising a mouth region having one or both of: a dental feature and an oral feature; partitioning the dataset into one or more subsets, wherein one subset is used as validation images for each remaining subset of the one or more subsets of training images, to provide one or more trained models; receiving a user input image of a user mouth region, wherein the user input image is a 3-channel image; converting the 3-channel image into a 1-channel image to provide a greyscale user input image; and analyzing the user mouth region of the greyscale user input image using the one or more trained models to provide a prediction of a presence or an absence of one or both of: the dental feature or the oral feature. ### Claim 58 (depends on 57) The method of claim 57, further comprising: generating an oral health score for each of the user input image corresponding to the prediction of the presence of or the absence of the one or both of: the dental feature or the oral feature, wherein the oral health score comprises an indication of one or more of: dental caries, periodontitis, gingivitis, fillings, toothbrush abrasion, dental erosion, teeth sensitivity, oral cancer, cracked or broken teeth, mouth sores, halitosis, abscess, congenital tooth conditions, tongue disease, and one or more cosmetic conditions.