# US 2023/0274191 A1 · Methods and systems for connecting food interests with food providers

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- Number: US 2023/0274191 A1
- Document ID: US20230274191A1
- Status: Published application
- Kind: A1
- Application number: 18/195,067
- Filed: 2023-05-09
- Published: 2023-08-31
- Earliest claimed: 2020-06-02
- Assignees: Kpn Innovations, LLC.
- Applicants: Kpn Innovations, LLC.
- Inventors: Kenneth Neumann
- Web page: https://patentgrep.com/patent/US20230274191A1

## CPC

- G06N 20/00: Machine learning
- G06Q 30/0631: {Recommending goods or services}
- G06Q 30/0255: {based on user history}
- G06Q 50/12: Hotels or restaurants
- G16H 20/60: relating to nutrition control, e.g. diets
- G16H 40/20: for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
- G16H 50/30: for calculating health indices; for individual health risk assessment
- 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
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

A system for, and method of, generating textual outputs based on descriptor classifications, including a computing device configured to receive a specified location and a nourishment intake theme from a remote device, generate, as a function of a group machine-learning model, a user group theme for the first remote device, identify at least a food provider located within the specified location, determine, as a function of an descriptor machine-learning model, a descriptor classification for the first remote device and generate at least an user interface element at the first remote device as a function of the descriptor classification.

## Claims

### Claim 1

A system for generating textual outputs based on descriptor classifications, the system comprising a computing device, the computing device designed and configured to:
receive an input from a first remote device of a plurality of remote devices, wherein the input comprises a specified location and a nourishment intake theme;
generate, as a function of a group machine-learning model, a user group theme for the first remote device, wherein the group machine-learning model receives the nourishment intake theme and the specified location as an input, and outputs the user group theme;
identify at least a food provider located within the specified location;
determine, as a function of a descriptor machine-learning model, a descriptor classification for the first remote device, wherein the advertisement machine-learning model receives the at least a food provider and the user group theme as input and outputs the descriptor classification; and
generate at least a user interface element at the first remote device as a function of the descriptor classification.

### Claim 2 (depends on 1)

The system of claim 1, wherein the computing device is further configured to:
receive food interest data from the first remote device; and
generate an interest group theme as a function of an interest machine-learning model, wherein the interest machine-learning model receives the food interest data and the specified location as input and outputs the interest group theme.

### Claim 3 (depends on 2)

The system of claim 2, wherein the computing device is further configured to:
determine a targeted theme using a targeted machine-learning model, wherein the targeted machine-learning model receives the at least a food provider and the interest group theme as inputs and outputs the targeted theme; and
generate the at least a user interface element at the first remote device as a function of the targeted theme.

### Claim 4 (depends on 1)

The system of claim 1, wherein computing device is configured to receive an availability datum.

### Claim 5 (depends on 4)

The system of claim 4, wherein the computing device is further configured to determine the at least an advertising material for the first remote device as a function of the advertisement theme and the availability datum.

### Claim 6 (depends on 1)

The system of claim 1, wherein computing device is configured to receive a promotional datum.

### Claim 7 (depends on 6)

The system of claim 6, wherein the computing device is further configured to generate the at least a user interface element at the first remote device as a function of the descriptor classification and the promotional datum.

### Claim 8 (depends on 1)

The system of claim 1, wherein the computing device is further configured to identify the at least a food provider as a function of a bid ranking.

### Claim 9 (depends on 1)

The system of claim 1, wherein the computing device is further configured to receive interface feedback.

### Claim 10 (depends on 9)

The system of claim 9, wherein the computing device is further configured to transmit the at least a user interface element to other remote devices based on the interface feedback.

### Claim 11

A method of generating textual outputs based on descriptor classifications, the method comprising:
receiving, by a computing device, an input from a first remote device of a plurality of remote devices, wherein the input comprises a specified location and a nourishment intake theme;
generating, by the computing device, a user group theme for the first remote device as a function of a group machine-learning model, wherein the group machine-learning model receives the nourishment intake theme and the specified location as an input, and outputs the user group theme;
identifying, by the computing device, at least a food providers located within the specified location;
determining, by the computing device, a descriptor classification for the first remote device as a function of a descriptor machine-learning model, wherein the descriptor machine-learning model receives the at least a food provider and the user group theme as input and outputs the descriptor classification; and
generating, by the computing device, at least a user interface element at the first remote device as a function of the descriptor classification.

### Claim 12 (depends on 11)

The method of claim 11, wherein the method further comprises:
receiving, by the computing device, food interest data from the first remote device; and
generating, by the computing device, an interest group theme as a function of an interest machine-learning model, wherein the interest machine-learning model receives the food interest data and the specified location as input and outputs the interest group theme.

### Claim 13 (depends on 12)

The method of claim 12, wherein the method further comprises:
determining, by the computing device, a targeted theme using a targeted machine-learning model, wherein the targeted machine-learning model receives the at least a food provider and the interest group theme as inputs and outputs the targeted theme; and
generating, by the computing device, the at least textual output at the first remote device as a function of the targeted theme.

### Claim 14 (depends on 11)

The method of claim 11, wherein the method further comprises receiving, by the computing device, an availability datum.

### Claim 15 (depends on 14)

The method of claim 14, wherein the method further comprises determining, by the computing device, the at least an advertising material for the first remote device as a function of the advertisement theme and the availability datum.

### Claim 16 (depends on 11)

The method of claim 11, wherein the method further comprises receiving, by the computing device, a promotional datum.

### Claim 17 (depends on 16)

The method of claim 16, wherein the method further comprises generating, by the computing device, the at least a user interface element at the first remote device as a function of the descriptor classification and the promotional datum.

### Claim 18 (depends on 11)

The method of claim 11, wherein the method further comprises identifying, by the computing device, the at least a food provider as a function of a bid ranking.

### Claim 19 (depends on 11)

The method of claim 11, wherein the method further comprises receiving, by the computing device, interface feedback.

### Claim 20 (depends on 19)

The method of claim 19, wherein the method further comprises transmitting, by the computing device, the at least a user interface element to other remote devices based on the interface feedback.