# US 10,872,412 B2 · Automatic graph scoring for neuropsychological assessments > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,872,412 B2 - Document ID: US10872412B2 - Status: Granted - Kind: B2 - Application number: 16/373,079 - Filed: 2019-04-02 - Published: 2019-10-03 - Granted: 2020-12-22 - Earliest claimed: 2018-04-02 - Assignees: Pearson Education, Inc. - Applicants: Pearson Education, Inc. - Inventors: Zhaodong Wang - Web page: https://patentgrep.com/patent/US10872412B2 ## CPC - G06V 10/764: using classification, e.g. of video objects - A61B 5/4076: {Diagnosing or monitoring particular conditions of the nervous system} - A61B 5/4088: {Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia} - A61B 5/7253: {characterised by using transforms} - A61B 5/7267: {involving training the classification device} - G06F 18/2163: {Partitioning the feature space} - G06F 18/217: {Validation; Performance evaluation; Active pattern learning techniques} - G06F 18/2431: Multiple classes - G06F 18/2453: non-linear, e.g. polynomial classifier - G06T 3/40: Scaling of whole images or parts thereof, e.g. expanding or contracting - G06T 7/0012: {Biomedical image inspection} - G06V 10/7715: {Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods} - G09B 19/00: Teaching not covered by other main groups of this subclass (teaching or practice apparatus for gun-aiming or gun-laying F41G3/26) - A61B 5/1124: {Determining motor skills} - A61B 5/1128: {using image analysis (A61B5/1127 takes precedence)} - G06T 2207/30242: Counting objects in image - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract Systems and methods of the present invention provide for: receiving a digital image data; modifying the digital image data to reduce a width of a feature within the digital image data; executing a dimension reduction process on the feature; storing a feature vector comprising: at least one feature for each of the received digital image data, and a correct or incorrect label associated with each feature vector; selecting the feature vector from a data store; training a classification software engine to classify each feature vector according to the label; classifying the image data as correct or incorrect according to a classification software engine; and generating an output labeling a second digital image data as correct or incorrect. ## Claims ### Claim 1 A system comprising a server, the server comprising a hardware computing device coupled to a network and including at least one processor executing within a memory instructions which, when executed, cause the system to: receive a plurality of image inputs, each comprising a digital image data; modify the digital image data to reduce a width of at least one feature within the digital image data, wherein the instructions further cause the system, for each of a first plurality of pixels within the digital image data to: identify a pixel within the first plurality of pixels; identify a total number of black pixels within: a second plurality of pixels immediately adjacent to the pixel; and a third plurality of pixels immediately adjacent to the second plurality of pixels; determine whether the total number of black pixels is greater than a threshold number; responsive to a determination that the total number of black pixels is greater than the threshold number, characterize the pixel as a black pixel; and responsive to a determination that the total number of black pixels is not greater than the threshold number, characterize the pixel as a white or an empty pixel; execute a dimension reduction process on the at least one feature within the digital image data; store, within a data store: a feature vector for each digital image data in the plurality of image input, the feature vector comprising the at least one feature of the digital image data; and a correct or incorrect label associated with each feature vector; select, from the data store, the feature vector for each digital image data; and train a classification software engine to classify each feature vector according to the label associated with the feature vector. ### Claim 2 (depends on 1) The system of claim 1, wherein the instructions further cause the system to: receive a second image input comprising a second digital image data; classify the second digital image data as correct or incorrect according to the classification software engine; and generate an output labeling the second digital image data as correct or incorrect. ### Claim 3 (depends on 1) The system of claim 1, wherein the instructions further cause the system to, for each of a first plurality of pixels within the digital image data: identify a pixel within the first plurality of pixels; identify a total number of black pixels within a second plurality of pixels immediately adjacent to the pixel and a third plurality of pixels immediately adjacent to the second plurality of pixels determine whether the total number of black pixels is greater than a threshold number; responsive to a determination that the total number of black pixels is greater than the threshold number, characterize the pixel as a black pixel; responsive to a determination that the total number of black pixels is not greater than the threshold number, characterize the pixel as a white or an empty pixel. ### Claim 4 (depends on 1) The system of claim 1, wherein the dimension reduction process includes execution of an orthogonal transformation to reduce a number of the at least one feature within the feature vector data for each digital image data. ### Claim 5 (depends on 1) The system of claim 1, wherein: each at least one feature is a pixel in the digital image data; and the dimension reduction algorithm reduces the number of pixels and features in the feature vector. ### Claim 6 (depends on 1) The system of claim 1, wherein the classification software engine uses the feature vector to train the model with a non-linear Radial Basis Function (RBF) kernel method. ### Claim 7 (depends on 1) The system of claim 1, wherein the classification software engine is trained by: partitioning the plurality of feature vectors into a plurality of folds; designating a fold within the plurality of folds as a validation dataset; designating each remaining fold in the plurality of folds as a training dataset; running a performance analysis for each of a plurality of subsets in the training dataset, the performance analysis comprising: performing an analysis on each of the plurality of training subsets in the training dataset; and validating the analysis against the validation dataset; designating each remaining fold in the plurality of folds as the validation dataset, and the validation dataset as being within the training dataset; and repeating the performance analysis for each remaining fold in the plurality of folds. ### Claim 8 A system comprising a server, the server comprising a hardware computing device coupled to a network and including at least one processor executing within a memory instructions which, when executed, cause the system to: receive an image input comprising a digital image data; modify the digital image data to reduce a width of at least one feature within the digital image data, wherein the instructions further cause the system, for each of a first plurality of pixels within the digital image data to: identify a pixel within the first plurality of pixels; identify a total number of black pixels within: a second plurality of pixels immediately adjacent to the pixel; and a third plurality of pixels immediately adjacent to the second plurality of pixels; determine whether the total number of black pixels is greater than a threshold number; responsive to a determination that the total number of black pixels is greater than the threshold number, characterize the pixel as a black pixel; responsive to a determination that the total number of black pixels is not greater than the threshold number, characterize the pixel as a white or an empty pixel; execute a dimension reduction process on the at least one feature within the digital image data; classify the digital image data as correct or incorrect according to a classification software engine; and generate an output labeling the digital image data as correct or incorrect. ### Claim 9 (depends on 8) The system of claim 8, wherein the instructions further cause the system to: receive a plurality of image inputs, each comprising the digital image data; store, within a data store: a feature vector for each digital image data in the plurality of image inputs, the feature vector comprising the at least one feature of the digital image data; and a correct or incorrect label associated with each feature vector; select, from the data store, the feature vector for each digital image data; and train the classification software engine to classify each feature vector according to the label associated with the feature vector. ### Claim 10 (depends on 8) The system of claim 8, wherein the computer executable instructions further cause the system to, for each of a first plurality of pixels within the digital image data: identify a pixel within the first plurality of pixels; identify a total number of black pixels within a second plurality of pixels immediately adjacent to the pixel and a third plurality of pixels immediately adjacent to the second plurality of pixels determine whether the total number of black pixels is greater than a threshold number; responsive to a determination that the total number of black pixels is greater than the threshold number, characterize the pixel as a black pixel; responsive to a determination that the total number of black pixels is not greater than the threshold number, characterize the pixel as a white or an empty pixel. ### Claim 11 (depends on 8) The system of claim 8, wherein the dimension reduction process includes execution of an orthogonal transformation to reduce a number of the at least one feature within the feature vector data for each digital image data. ### Claim 12 (depends on 9) The system of claim 9, wherein: each at least one feature is a pixel in the digital image data; and the dimension reduction algorithm reduces the number of pixels and features in the feature vector. ### Claim 13 (depends on 8) The system of claim 8, wherein the classification software engine uses the feature vector to train the model with a non-linear Radial Basis Function (RBF) kernel method. ### Claim 14 (depends on 9) The system of claim 9, wherein the classification software engine is trained by: partitioning the plurality of feature vectors into a plurality of folds; designating a fold within the plurality of folds as a validation dataset; designating each remaining fold in the plurality of folds as a training dataset; running a performance analysis for each of a plurality of subsets in the training dataset, the performance analysis comprising: performing an analysis on each of the plurality of training subsets in the training dataset; and validating the analysis against the validation dataset; designating each remaining fold in the plurality of folds as the validation dataset, and the validation dataset as being within the training dataset; and repeat the performance analysis for each remaining fold in the plurality of folds. ### Claim 15 A method comprising the steps of: receiving, by a server comprising a hardware computing device coupled to a network and comprising at least one processor executing computer-executable instructions within a memory, a plurality of image inputs, each including a digital image data; modifying, by the server hardware computing device, the digital image data to reduce a width of at least one feature within the digital image data, further comprising the steps of, for each of a first plurality of pixels within the digital image data: Identifying, by the server hardware computing device, a pixel within the first plurality of pixels; Identifying, by the server hardware computing device, a total number of black pixels within: a second plurality of pixels immediately adjacent to the pixel; and a third plurality of pixels immediately adjacent to the second plurality of pixels; determining, by the server hardware computing device, whether the total number of black pixels is greater than a threshold number; responsive to a determination that the total number of black pixels is greater than the threshold number, characterizing, by the server hardware computing device, the pixel as a black pixel; responsive to a determination that the total number of black pixels is greater than the threshold number, characterizing, by the server hardware computing device, the pixel as a white or an empty pixel; executing, by the server hardware computing device, a dimension reduction process on the at least one feature within the digital image data; storing, by the server hardware computing device, within a data store: a feature vector for each digital image data in the plurality of transmissions, the feature vector comprising the at least one feature of the digital image data; and a correct or incorrect label associated with each feature vector; selecting, by the server hardware computing device, from the data store, the feature vector for each digital image data; training, by the server hardware computing device, a classification software engine to classify each feature vector according to the label associated with the feature vector. ### Claim 16 (depends on 15) The method of claim 15, further comprising the steps of: receiving, by the server hardware computing device, a second transmission comprising a second digital image data; classifying, by the server hardware computing device, the second digital image data as correct or incorrect according to the classification software engine; and generating, by the server hardware computing device, an output labeling the second digital image data as correct or incorrect. ### Claim 17 (depends on 15) The method of claim 15, further comprising the steps of, for each of a first plurality of pixels within the digital image data: Identifying, by the server hardware computing device, a pixel within the first plurality of pixels; Identifying, by the server hardware computing device, a total number of black pixels within a second plurality of pixels immediately adjacent to the pixel and a third plurality of pixels immediately adjacent to the second plurality of pixels determining, by the server hardware computing device, whether the total number of black pixels is greater than a threshold number; responsive to a determination that the total number of black pixels is greater than the threshold number, characterizing, by the server hardware computing device, the pixel as a black pixel; responsive to a determination that the total number of black pixels is greater than the threshold number, characterizing, by the server hardware computing device, the pixel as a white or an empty pixel. ### Claim 18 (depends on 15) The method of claim 15, wherein the dimension reduction process includes execution of an orthogonal transformation to reduce a number of the at least one feature within the feature vector data for each digital image data. ### Claim 19 (depends on 15) The method of claim 15, wherein: each at least one feature is a pixel in the digital image data; and the dimension reduction algorithm reduces the number of pixels and features in the feature vector. ### Claim 20 (depends on 15) The method of claim 15, wherein the classification software engine uses the feature vector to train the model with a non-linear Radial Basis Function (RBF) kernel method.