# US 11,908,583 B2 · Apparatus and method for determining toxic load quantifiers

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- Number: US 11,908,583 B2
- Document ID: US11908583B2
- Status: Granted
- Kind: B2
- Application number: 17/734,449
- Filed: 2022-05-02
- Published: 2023-11-02
- Granted: 2024-02-20
- Earliest claimed: 2022-05-02
- Assignees: Kpn Innovations, LLC.
- Applicants: Kpn Innovations, LLC.
- Inventors: Kenneth Neumann
- Web page: https://patentgrep.com/patent/US11908583B2

## CPC

- G16H 50/30: for calculating health indices; for individual health risk assessment
- G06N 20/00: Machine learning
- G06Q 30/0282: Rating or review of business operators or products
- G16B 40/00: ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- G16H 20/60: relating to nutrition control, e.g. diets
- G16H 30/40: for processing medical images, e.g. editing
- G16H 40/63: for local operation
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 7/02: using fuzzy logic (computing arrangements based on biological models G06N3/00; computing arrangements using knowledge-based models G06N5/00)
- G16B 20/00: ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY

## Abstract

In an aspect an apparatus for determining toxic load quantifiers is presented. An apparatus includes at least a processor and a memory communicatively connected to the at least a processor. A memory contains instructions configuring at least a processor to receive user input comprising user data. At least a processor is configured to generate a query as a function of user data. At least a processor is configured to obtain query results as a function of a query. At least a processor is configured to determine a toxic load quantifier of a query results of query results as a function of a toxicity criterion and user data. At least a processor is configured to display a toxic load quantifier of query results to a user.

## Claims

### Claim 1

An apparatus for determining toxic load quantifiers, comprising:
at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:
receive user input comprising first user data obtained from a physically extracted sample of the user, wherein the physically extracted sample of the user indicates an absorption ability of the user;
determine a toxin profile, wherein determining the toxin profile comprises:
receiving first training data correlating environmental elements to toxic element exposure levels;
training an environmental exposure classifier using the first training data and a first machine learning algorithm, wherein the environmental exposure classifier is configured to input first user data obtained from the physically extracted sample of the user and output toxin profiles; and
determining as a function of the first user data and the environmental exposure classifier, the toxin profile;
generate a query as a function of the first user data, wherein the query comprises an article of interest;
obtain at least a query result as a function of the query, wherein obtaining the at least query result comprises:
generating an objective function; and
optimizing the objective function as a function of the article of interest;
determine, as a function of a toxicity criterion and the first user data, a toxic load quantifier of the at least a query result, wherein the toxic load quantifier is based on an absorption rate of toxic elements in the article of interest indicated by the physically extracted sample of the user, wherein determining the toxic load quantifier comprises:
receiving second training data correlating query results to toxic load rankings;
training a toxic load machine learning model using the second training data and a second machine learning algorithm, wherein the toxic load machine learning model is configured to input query results and output toxic load rankings; and
determining, as a function of the obtained query result and the toxic load machine learning model, a toxic load ranking; and
display the toxic load quantifier to a user.

### Claim 2 (depends on 1)

The apparatus of claim 1, wherein a toxic element of the toxic load quantifier includes carcinogens.

### Claim 3 (depends on 1)

The apparatus of claim 1, wherein a toxic element of the toxic load quantifier includes toxins that affect a user's immune system.

### Claim 4 (depends on 1)

The apparatus of claim 1, wherein a toxic element of the toxic load quantifier includes toxins that affect DNA of a user.

### Claim 5 (depends on 1)

The apparatus of claim 1, wherein the at least a processor is further configured to generate a cumulative toxin exposure model.

### Claim 6 (depends on 1)

The apparatus of claim 1, wherein the at least a processor is further configured to determine the toxic load quantifier specific to a user.

### Claim 7 (depends on 1)

The apparatus of claim 1, wherein the at least a processor is further configured to generate a detoxing protocol.

### Claim 8 (depends on 7)

The apparatus of claim 7, wherein the at least a processor is configured to:
receive second user data; and
adjust the detoxing protocol as a function of the second user data.

### Claim 9 (depends on 1)

The apparatus of claim 1, wherein the at least a processor is further configured to generate a prophylaxis protocol as a function of the toxic load quantifier.

### Claim 10 (depends on 1)

The apparatus of claim 1, wherein the at least a processor is further configured to classify environmental exposures to toxic element exposure levels using the environmental exposure classifier.

### Claim 11 (depends on 1)

The apparatus of claim 1, wherein the first user data includes an article of interest, and the at least a processor is further configured to display alternative articles of interest as a function of the toxic load quantifier.

### Claim 12 (depends on 1)

The apparatus of claim 1, wherein the first user data further includes an article of interest, and the at least a processor is further configured to determine a toxic load impact of the article of interest.

### Claim 13 (depends on 1)

The apparatus of claim 1, wherein the at least a processor is further configured to generate the toxin profile as a function of the user input and the toxic load quantifier.

### Claim 14

A method of using a computing device for determining toxic load quantifiers, comprising:
receiving user input comprising first user data obtained from a physically extracted sample of the user, wherein the physically extracted sample of the user indicates an absorption ability of the user;
determining a toxin profile, wherein determining the toxin profile comprises:
receiving first training data correlating environmental elements to toxic element exposure levels;
training an environmental exposure classifier using the first training data and a first machine learning algorithm, wherein the environmental exposure classifier is configured to input first user data obtained from the physically extracted sample of the user and output toxin profiles; and
determining as a function of the first user data and the environmental exposure classifier, the toxin profile;
generating a query as a function of the first user data, wherein the query comprises an article of interest;
obtaining at least a query result as a function of the query, wherein obtaining the at least query result further comprises:
generating an objective function; and
optimizing the objective function as a function of the article of interest;
determining, as a function of a toxicity criterion and the first user data, a toxic load quantifier of the at least a query result, wherein the toxic load quantifier is based on an absorption rate of toxic elements in the article of interest indicated by the physically extracted sample of the user; wherein determining the toxic load quantifier comprises:
receiving second training data correlating query results to toxic load rankings;
training a toxic load machine learning model using the second training data and a second machine learning algorithm, wherein the toxic load machine learning model is configured to input query results and output toxic load rankings; and
determining, as a function of the obtained query result and the toxic load machine learning model, a toxic load ranking; and
displaying the toxic load quantifier to a user.

### Claim 15 (depends on 14)

The method of claim 14, wherein displaying further comprises displaying alternative articles of interest to the user as a function of the toxic load quantifier of the query results.

### Claim 16 (depends on 14)

The method of claim 14, wherein determining further comprises determining a toxic load impact of the article of interest of the at least a query result.