# US 11,908,135 B2 · Artificial intelligence methods and systems for analyzing imagery

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- Number: US 11,908,135 B2
- Document ID: US11908135B2
- Status: Granted
- Kind: B2
- Application number: 17/884,658
- Filed: 2022-08-10
- Published: 2022-12-08
- Granted: 2024-02-20
- Earliest claimed: 2020-04-01
- Assignees: Kpn Innovations, LLC.
- Applicants: Kpn Innovations, LLC.
- Inventors: Kenneth Neumann
- Web page: https://patentgrep.com/patent/US11908135B2

## 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 apparatus for analyzing imagery, the apparatus comprising:
at least a processor; and
a memory connectively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:
receive a first plurality of photographs from a social networking platform related to a human subject;
receive a second plurality of photographs from a remote device related to the human subject;
analyze the first plurality of photographs and the second plurality of photographs to identify a conditional indicator related to the human subject, based on entries contained within an expert database, wherein identifying the conditional indicator comprises identifying a conditional indicator group related to the human subject;
generate a classification process, wherein the classification process utilizes the conditional indicator as an input and outputs a conditional profile; and
determine a conditional status of the human subject as a function of the conditional profile.

### Claim 2 (depends on 1)

The apparatus of claim 1, wherein the expert database contains a plurality of data entries wherein each entry of the plurality of data entries comprises an expert submission and an expert identifier.

### Claim 3 (depends on 1)

The apparatus of claim 1, wherein the conditional indicator is a determinant of a health of the human subject.

### Claim 4 (depends on 1)

The apparatus of claim 1, wherein the conditional profile includes information describing at least a condition relating to the human subject.

### Claim 5 (depends on 1)

The apparatus of claim 1, wherein the memory contains instructions further configuring the processor to:
retrieve an element of user physiological data, which includes a physically extracted sample; and
generate the classification process as a function of the element of user physiological data.

### Claim 6 (depends on 1)

The apparatus of claim 1, wherein the memory contains instructions further configuring the processor to:
calculate 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 as a function of the machine-learning process.

### Claim 7 (depends on 1)

The apparatus of claim 1, wherein the conditional status further comprises a disease likelihood score.

### Claim 8 (depends on 1)

The apparatus of claim 1, wherein the conditional status further comprises a treatment identifier.

### Claim 9 (depends on 1)

The apparatus of claim 1, wherein the memory contains instructions further configuring the processor 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 processor, a first plurality of photographs from a social networking platform related to a human subject;
receiving, by the processor, a second plurality of photographs from a remote device related to a human subject;
analyzing, by the processor, the first plurality of photographs and the second plurality of photographs to identify a conditional indicator related to the human subject based on input contained within an expert database, wherein identifying the conditional indicator comprises identifying a conditional indicator group related to the human subject;
generating, by the processor, a classification process utilizing the conditional indicator, wherein the classification process utilizes the conditional indicator as an input and outputs a conditional profile; and
determining, by the processor, a conditional status of the human subject as a function of the conditional profile.

### Claim 11 (depends on 10)

The method of claim 10, wherein the expert database contains a plurality of data entries wherein each entry of the plurality of data entries comprises an expert submission and an expert identifier.

### Claim 12 (depends on 10)

The method of claim 10, wherein the conditional indicator is a determinant of a health of the human subject.

### Claim 13 (depends on 10)

The method of claim 10, wherein the conditional profile includes information describing at least a condition relating to the human subject.

### Claim 14 (depends on 10)

The method of claim 10, further comprising:
retrieving, by the processor, an element of user physiological data, which includes a physically extracted sample; and
generating, by the processor, the classification process as a function of the element of user physiological data.

### Claim 15 (depends on 10)

The method of claim 10, further comprising:
calculating, by the processor, a machine-learning process, wherein the machine-learning process utilizes the conditional profile as an input and outputs the conditional status; and
determining, by the processor, the conditional status of the human subject as a function of 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, by the processor, the conditional status of the human subject to a remote device operated by an informed advisor;
receiving, by the processor, an input submitted to the remote device in response to the conditional status of the human subject; and
updating, by the processor, the conditional status utilizing the input.