# US 11,443,424 B2 · Artificial intelligence methods and systems for analyzing imagery > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,443,424 B2 - Document ID: US11443424B2 - Status: Granted - Kind: B2 - Application number: 16/837,007 - Filed: 2020-04-01 - Published: 2021-10-07 - Granted: 2022-09-13 - Earliest claimed: 2020-04-01 - Assignees: Kpn Innovations, LLC. - Applicants: KPN Innovations, LLC - Inventors: Kenneth Neumann - Web page: https://patentgrep.com/patent/US11443424B2 ## CPC - G06T 7/0012: {Biomedical image inspection} - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/09: Supervised learning - G06N 5/04: Inference or reasoning models - G06N 20/00: Machine learning - 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 - G06N 3/045: Combinations of networks - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - G06N 20/20: Ensemble learning - G06T 2207/20081: Training; Learning - G06T 2207/30008: Bone - G06T 2207/30016: Brain - G06T 2207/30101: Blood vessel; Artery; Vein; Vascular - G16B 20/00: ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations - Y02A 90/10: Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - 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 - G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY - Y02A: TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE ## Abstract An artificial intelligence system for analyzing imagery, the system comprising a computing device, the computing device designed and configured to receive a plurality of photographs related to a human subject; analyze the plurality of photographs to identify a conditional indicator contained within the plurality of photographs; generate a classification algorithm utilizing the conditional indicator, wherein the classification algorithm utilizes the conditional indicator as an input and outputs a conditional profile; and determine a conditional status of the human subject utilizing the conditional profile. ## Claims ### Claim 1 An artificial intelligence system for analyzing imagery, the system comprising a computing device, the computing device designed and configured to: receive a plurality of photographs related to a human subject; receive an element of user physiological data; analyze the plurality of photographs to identify a conditional indicator contained within the plurality of photographs; generate a classification algorithm, wherein the classification algorithm utilizes: the conditional indicator and the element of physiological data as inputs, wherein the element of physiological data is classified to a disease state category from a plurality of disease state categories; and outputs a conditional profile; and determine a conditional status of the human subject utilizing the conditional profile. ### Claim 2 (depends on 1) The system of claim 1, wherein the computing device is further configured to receive at an image capture device located on the computing device a wireless transmission from a remote device containing the plurality of photographs related to a human subject. ### Claim 3 (depends on 1) The system of claim 1, wherein the computing device is further configured to receive the plurality of photographs related to the human subject from a social networking platform. ### Claim 4 (depends on 1) The system of claim 1, wherein the computing device is further configured to: identify a conditional indicator group identified within the plurality of photographs; and generate a label, identifying the conditional indicator group. ### Claim 5 (depends on 4) The system of claim 4, wherein the computing device is further configured to: identify information missing from a conditional indicator group; transmit a request to a remote device operated by the human subject, to obtain more information relating to the conditional indicator group; and receive from the remote device operated by the human subject a response containing at least an element of information. ### Claim 6 (depends on 1) The system of claim 1, wherein the computing device is further configured to: perform a machine-learning process, wherein the machine-learning process utilizes the conditional profile as an input and outputs the conditional status; and determine the conditional status of the human subject utilizing the machine-learning process. ### Claim 7 (depends on 1) The system of claim 1, wherein the conditional status further comprises a disease likelihood score. ### Claim 8 (depends on 1) The system of claim 1, wherein the conditional status further comprises a treatment identifier. ### Claim 9 (depends on 1) The system of claim 1, wherein the computing device is further configured to: transmit the conditional status of the human subject to a remote device operated by an informed advisor; receive an input submitted to the remote device in response to the conditional status of the human subject; and update the conditional status utilizing the input. ### Claim 10 An artificial intelligence method of analyzing imagery, the method comprising: receiving by a computing device, a plurality of photographs related to a human subject; receiving by the computing device, an element of user physiological data; analyzing by the computing device, the plurality of photographs to identify a conditional indicator contained within the plurality of photographs; generating by the computing device, a classification algorithm utilizing the conditional indicator, wherein the classification algorithm utilizes the conditional indicator and the element of physiological data as inputs, wherein the element of physiological data is classified to a disease state category from a plurality of disease state categories; and outputs a conditional profile; and determining by the computing device, a conditional status of the human subject utilizing the conditional profile. ### Claim 11 (depends on 10) The method of claim 10, wherein receiving the plurality of photographs further comprises receiving at an image capture device located on the computing device a wireless transmission from a remote device containing the plurality of photographs related to a human subject. ### Claim 12 (depends on 10) The method of claim 10, wherein receiving the plurality of photographs related to the human subject further comprises receiving the plurality of photographs related to the human subject from a social networking platform. ### Claim 13 (depends on 10) The method of claim 10, wherein analyzing the plurality of photographs further comprises: identifying a conditional indicator group identified within the plurality of photographs; and generating a label, identifying the conditional indicator group. ### Claim 14 (depends on 13) The method of claim 13 further comprising: identifying information missing from a conditional indicator group; transmitting a request to a remote device operated by the human subject, to obtain more information relating to the conditional indicator group; and receiving from the remote device operated by the human subject a response containing more information. ### Claim 15 (depends on 10) The method of claim 10, wherein determining the conditional status further comprises: calculating a machine-learning process, wherein the machine-learning process utilizes the conditional profile as an input and outputs the conditional status; and determining the conditional status of the human subject utilizing the machine-learning process. ### Claim 16 (depends on 10) The method of claim 10, wherein the conditional status further comprises a disease likelihood score. ### Claim 17 (depends on 10) The method of claim 10, wherein the conditional status further comprises a treatment identifier. ### Claim 18 (depends on 10) The method of claim 10 further comprising: transmitting the conditional status of the human subject to a remote device operated by an informed advisor; receiving an input generated by the informed advisor in response to the conditional status of the human subject; and updating the conditional status utilizing the input generated by the informed advisor.