# US 2021/0241065 A1 · Classifying Discipline-Specific Content Using a General-Content Brain-Response Model > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0241065 A1 - Document ID: US20210241065A1 - Status: Published application - Kind: A1 - Application number: 17/148,607 - Filed: 2021-01-14 - Published: 2021-08-05 - Earliest claimed: 2018-12-04 - Assignees: Brainvivo Ltd. - Applicants: Brainvivo Ltd. - Inventors: Yaniv Assaf; Assaf Horowitz - Web page: https://patentgrep.com/patent/US20210241065A1 ## CPC - A61B 5/4064: {Evaluating the brain (for intracranial pressure A61B5/031; for cerebral blood gases A61B5/14553; using EEG A61B5/369)} - A61B 5/055: involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging - G06F 3/015: {Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection} - G06F 18/213: Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods - G06F 18/22: Matching criteria, e.g. proximity measures - G06F 18/2431: Multiple classes - G06N 3/004: Artificial life, i.e. computing arrangements simulating life - G06N 3/09: Supervised learning - G06N 20/00: Machine learning - G06V 10/75: Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries - G01R 33/4806: {Functional imaging of brain activation} - G01R 33/56341: {Diffusion imaging} - G06N 3/08: Learning methods - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06V 2201/03: Recognition of patterns in medical or anatomical images - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12) ## Abstract A content classification method includes receiving a set of content items from categories of a specific discipline, and extracting respective features from each content item. A labeling of the content items of the specific discipline is received, performed by human viewers, the labeling indicating a respective category assigned to the content item by the human viewers. A general-content brain-response model is uploaded, the model estimated using measurements of brains of humans presented with a general-content database defined using a set of features and includes a mapping between the set of features and a set of extracted brain activities. The model is applied to the extracted features, to calculate, using the labeling, a set of brain-responses for the specific discipline. Given a new content item associated with the discipline, a category of the discipline best matching the new content item is estimated, based on the model and the discipline-specific brain responses. ## Claims ### Claim 1 A content classification method, comprising: receiving a set of content items belonging to multiple predefined categories of a specific discipline, and extracting respective features from each content item of the specific discipline; receiving a labeling of the content items of the specific discipline, performed by one or more human viewers, the labeling indicating, for each content item, a respective category assigned to the content item by the one or more human viewers from among the multiple predefined categories; uploading a general-content brain-response model estimated using measurements of brains of humans presented with a general-content database, wherein the general-content database is defined using a set of features and comprises a mapping between the set of features and a set of extracted brain activities; applying the general-content brain-response model to the extracted features, to calculate, using the labeling, a set of per-category brain-responses for the specific discipline; and given a new content item associated with the discipline, estimating a category that best matches the new content item from among the multiple predefined categories, based on the general-content brain-response model and the discipline-specific brain responses. ### Claim 2 (depends on 1) The content classification method according to claim 1, wherein estimating the category comprises: extracting a plurality of the features from the new content item; applying the general-content brain-response model to the features extracted from the new content item, to calculate a new content brain-response; and using the set of discipline-specific brain responses and the new content brain-response, estimating the category that best matches the new content item. ### Claim 3 (depends on 2) The content classification method according to claim 2, wherein estimating the category comprises estimating a respective set of distances, in a brain activity coordinate system, between the new content brain-response and the discipline-specific brain responses, and, using the labelling, classifying the new content item to one of the predefined categories according to the set of distances. ### Claim 4 (depends on 1) The content classification method according to claim 1, wherein estimating the category comprises calculating a respective set of probabilities that the new content item has a same label as any one of the given categories, and classifying the new content item to one of the predefined categories according to the calculated set of probabilities. ### Claim 5 (depends on 1) The content classification method according to claim 1, wherein extracting the features comprises omitting from the extracted features one or more of the predefined features that are deemed to be statistically insignificant. ### Claim 6 (depends on 1) The content classification method according to claim 1, wherein the extracted features comprise at least one of shades of colors, characteristic spatial frequencies, contrast levels, and prevalence. ### Claim 7 (depends on 1) The content classification method according to claim 1, and comprising deriving the general-content brain response model using a statistical model that is one of linear regression and non-linear regression. ### Claim 8 (depends on 1) The content classification method according to claim 1, wherein the measurements of brains of humans comprise brain connectivity matrices. ### Claim 9 (depends on 8) The content classification method according to claim 8, wherein the brain connectivity matrices include connectivity-matrix weights based upon micro-structure estimates of brain tissue to form a brain connectome. ### Claim 10 (depends on 1) The content classification method according to claim 1, wherein the measurements of brains of humans are modeled based upon cognitive layers combined with a connectivity association matrix. ### Claim 11 (depends on 1) The content classification method according to claim 1, wherein the content items of the specific discipline comprise images of semiconductor dies, wherein the categories are predefined quality bins, and wherein the labeling by the human review comprises assignment each image as representing a die belonging to one of predefined quality bins. ### Claim 12 (depends on 11) The content classification method according to claim 11, and comprising deciding on a use of a semiconductor die whose image was classified as representing a die belonging to one of predefined bins. ### Claim 13 The content classification method according to 1, wherein the brain measurements are performed by one or more of anatomical Magnetic Resonance Imaging (MRI), Diffusion Tensor Imaging (DTI), Functional MRI (fMRI), Electroencephalogram (EEG), Magnetoencephalogram (MEG), Infrared Imaging, Ultraviolet Imaging, Computed Tomography (CT), Brain Mapping Ultrasound, In-Vivo Cellular Data, In-Vivo Molecular data, genomic data, and optical imaging. ### Claim 14 (depends on 1) The content classification method according to claim 1, wherein the labeling comprises labeling of at least one of a sequence of frames, images, sounds, tactile signals, odors, tastes, and abstract content type. ### Claim 15 (depends on 14) The content classification method according to claim 14, wherein the abstract content type comprises feelings. ### Claim 16 (depends on 1) The content classification method according to claim 1, wherein the features are represented as a first space, wherein the set of brain activities is represented as a second space, and wherein the general-content brain-response model is defined as a linear transformation between the first and second spaces. ### Claim 17 A content classification apparatus, comprising: an interface, which is configured to: receive a set of content items belonging to multiple predefined categories of a specific discipline, and extracting respective features from each content item of the specific discipline; and receive a labeling of the content items of the specific discipline, performed by one or more human viewers, the labeling indicating, for each content item, a respective category assigned to the content item by the one or more human viewers from among the multiple predefined categories; and a processor, which is configured to: upload a general-content brain-response model estimated using measurements of brains of humans presented with a general-content database, wherein the general-content database is defined using a set of features and comprises a mapping between the set of features and a set of extracted brain activities; apply the general-content brain-response model to the extracted features, to calculate, using the labeling, a set of per-category brain-responses for the specific discipline; and given a new content item associated with the discipline, estimate a category that best matches the new content item from among the multiple predefined categories, based on the general-content brain-response model and the discipline-specific brain responses. ### Claim 18 (depends on 1) The content classification apparatus according to claim 1, wherein the processor is configured to estimate the category by: extracting a plurality of the features from the new content item; applying the general-content brain-response model to the features extracted from the new content item, to calculate a new content brain-response; and using the set of discipline-specific brain responses and the new content brain-response, estimating the category that best matches the new content item. ### Claim 19 (depends on 18) The content classification apparatus according to claim 18, wherein the processor is configured to estimate the category by estimating a respective set of distances, in a brain activity coordinate system, between the new content brain-response and the discipline-specific brain responses, and, using the labelling, classifying the new content item to one of the predefined categories according to the set of distances. ### Claim 20 (depends on 17) The content classification apparatus according to claim 17, wherein the processor is configured to estimate the category by calculating a respective set of probabilities that the new content item has a same label as any one of the given categories, and classifying the new content item to one of the predefined categories according to the calculated set of probabilities. ### Claim 21 (depends on 17) The content classification apparatus according to claim 17, wherein the processor is configured to extract the features by omitting from the extracted features one or more of the predefined features that are deemed to be statistically insignificant. ### Claim 22 (depends on 17) The content classification method according to claim 17, wherein the extracted features comprise at least one of shades of colors, characteristic spatial frequencies, contrast levels, and prevalence. ### Claim 23 (depends on 17) The content classification apparatus according to claim 17, wherein the processor is further configured to derive the general-content brain response model using a statistical model that is one of linear regression and non-linear regression. ### Claim 24 (depends on 17) The content classification apparatus according to claim 17, wherein the measurements of brains of humans comprise brain connectivity matrices. ### Claim 25 (depends on 24) The content classification apparatus according to claim 24, wherein the brain connectivity matrices include connectivity-matrix weights based upon micro-structure estimates of brain tissue to form a brain connectome. ### Claim 26 (depends on 17) The content classification apparatus according to claim 17, wherein the measurements of brains of humans are modeled based upon cognitive layers combined with a connectivity association matrix. ### Claim 27 (depends on 17) The content classification apparatus according to claim 17, wherein the content items of the specific discipline comprise images of semiconductor dies, wherein the categories are predefined quality bins, and wherein the labeling by the human review comprises assignment each image as representing a die belonging to one of predefined quality bins. ### Claim 28 (depends on 27) The content classification apparatus according to claim 27, wherein the processor is further configured to deciding on a use of a semiconductor die whose image was classified as representing a die belonging to one of predefined bins. ### Claim 29 (depends on 17) The content classification apparatus according to claim 17, wherein the brain measurements are performed by one or more of anatomical Magnetic Resonance Imaging (MRI), Diffusion Tensor Imaging (DTI), Functional MRI (fMRI), Electroencephalogram (EEG), Magnetoencephalogram (MEG), Infrared Imaging, Ultraviolet Imaging, Computed Tomography (CT), Brain Mapping Ultrasound, In-Vivo Cellular Data, In-Vivo Molecular data, genomic data, and optical imaging. ### Claim 30 (depends on 17) The content classification apparatus according to claim 17, wherein the labeling comprises labeling of at least one of a sequence of frames, images, sounds, tactile signals, odors, tastes, and abstract content type. ### Claim 31 (depends on 30) The content classification apparatus according to claim 30, wherein the abstract content type comprises feelings. ### Claim 32 (depends on 17) The content classification apparatus according to claim 17, wherein the features are represented as a first space, wherein the set of brain activities is represented as a second space, and wherein the general-content brain-response model is defined as a linear transformation between the first and second spaces.