# US 2026/0087699 A1 · Location modeling for localized object insertion

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- Number: US 2026/0087699 A1
- Document ID: US20260087699A1
- Status: Published application
- Kind: A1
- Application number: 18/991,199
- Filed: 2024-12-20
- Published: 2026-03-26
- Earliest claimed: 2024-09-24
- Assignees: QUALCOMM Incorporated
- Applicants: QUALCOMM Incorporated
- Inventors: Jooyeol YUN; Davide ABATI; Mohamed OMRAN; Auke Joris WIGGERS
- Web page: https://patentgrep.com/patent/US20260087699A1

## CPC

- G06T 11/60: Creating or editing images; Combining images with text
- G06V 10/764: using classification, e.g. of video objects
- G06V 10/82: using neural networks
- G06V 20/20: in augmented reality scenes
- G06V 20/70: Labelling scene content, e.g. deriving syntactic or semantic representations
- G06T 2200/24: involving graphical user interfaces [GUIs]
- G06T 2210/12: Bounding box
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

Systems and techniques are described herein for determining bounding box coordinates. For example, a computing device can process an image to generate a first plurality of tokens associated with the image. The computing device can process, using a first machine learning model, the first plurality of tokens and a class token associated with a class of an object to generate a probability distribution associated with coordinates of a bounding box within the image. The computing device can determine, based on the probability distribution, target coordinates to position the bounding box within the image.

## Claims

### Claim 1

An apparatus for image processing, the apparatus comprising:
at least one memory; and
at least one processor coupled to the at least one memory and configured to:
process an image to generate a first plurality of tokens associated with the image;
process, using a first machine learning model, the first plurality of tokens and a class token associated with a class of an object to generate a probability distribution associated with coordinates of a bounding box within the image; and
determine, based on the probability distribution, target coordinates to position the bounding box within the image.

### Claim 2 (depends on 1)

The apparatus of claim 1, wherein the at least one processor is configured to:
add a visual representation of the object to the image within the target coordinates of the bounding box.

### Claim 3 (depends on 2)

The apparatus of claim 2, wherein the at least one processor is configured to add the visual representation of the object using a second machine learning model.

### Claim 4 (depends on 1)

The apparatus of claim 1, wherein the first machine learning model is a transformer-based neural network model.

### Claim 5 (depends on 1)

The apparatus of claim 1, wherein the first machine learning model is trained using training data including a plurality of images with preset bounding boxes associated with one or more class of objects.

### Claim 6 (depends on 5)

The apparatus of claim 5, wherein the training is on-device training.

### Claim 7 (depends on 5)

The apparatus of claim 5, wherein the at least one processor is configured to:
generate a second plurality of tokens associated with coordinates of at least one bounding box of the preset bounding boxes.

### Claim 8 (depends on 7)

The apparatus of claim 7, wherein the second plurality of tokens comprises four tokens associated with the at least one bounding box, including a first x-coordinate token, a second x-coordinate token, a first y-coordinate token, and a second y-coordinate token.

### Claim 9 (depends on 8)

The apparatus of claim 8, wherein at least one class token associated with the at least one bounding box immediately precedes one of the first x-coordinate token or the first y-coordinate token.

### Claim 10 (depends on 1)

The apparatus of claim 1, wherein the at least one processor is configured to:
process a user selection of the object to generate the class token, wherein the user selection is represented as a one-hot class embedding vector.

### Claim 11 (depends on 1)

The apparatus of claim 1, wherein the probability distribution is a conditional probability distribution representing a probability each coordinate of the bounding box is located at a location within the image based on a location of other coordinates of the bounding box.

### Claim 12 (depends on 11)

The apparatus of claim 11, wherein the coordinates of the bounding box are associated with corners of the bounding box.

### Claim 13 (depends on 12)

The apparatus of claim 12, wherein the probability distribution is a histogram associated with probabilities of one or more corners of the bounding box being placed at one or more coordinates within the image.

### Claim 14 (depends on 1)

The apparatus of claim 1, wherein the target coordinates include x-y coordinates of a coordinate system associated with the image.

### Claim 15 (depends on 1)

The apparatus of claim 1, further comprising at least one camera configured to capture the image.

### Claim 16

A method for determining bounding box coordinates, the method comprising:
processing an image to generate a first plurality of tokens associated with the image;
processing, using a first machine learning model, the first plurality of tokens and a class token associated with a class of an object to generate a probability distribution associated with coordinates of a bounding box within the image; and
determining, based on the probability distribution, target coordinates to position the bounding box within the image.

### Claim 17 (depends on 16)

The method of claim 16, further comprising:
adding a visual representation of the object to the image within the target coordinates of the bounding box.

### Claim 18 (depends on 17)

The method of claim 17, further comprising adding the visual representation of the object using a second machine learning model.

### Claim 19 (depends on 16)

The method of claim 16, wherein the first machine learning model is a transformer-based neural network model.

### Claim 20 (depends on 16)

The method of claim 16, wherein the first machine learning model is trained using training data including a plurality of images with preset bounding boxes associated with one or more class of objects.

### Claim 21 (depends on 20)

The method of claim 20, wherein the training is on-device training.

### Claim 22 (depends on 20)

The method of claim 20, further comprising:
generating a second plurality of tokens associated with coordinates of at least one bounding box of the preset bounding boxes.

### Claim 23 (depends on 22)

The method of claim 22, wherein the second plurality of tokens comprises four tokens associated with the at least one bounding box, including a first x-coordinate token, a second x-coordinate token, a first y-coordinate token, and a second y-coordinate token.

### Claim 24 (depends on 23)

The method of claim 23, wherein at least one class token associated with the at least one bounding box immediately precedes one of the first x-coordinate token or the first y-coordinate token.

### Claim 25 (depends on 16)

The method of claim 16, further comprising:
processing a user selection of the object to generate the class token, wherein the user selection is represented as a one-hot class embedding vector.

### Claim 26 (depends on 16)

The method of claim 16, wherein the probability distribution is a conditional probability distribution representing a probability each coordinate of the bounding box is located at a location within the image based on a location of other coordinates of the bounding box.

### Claim 27 (depends on 26)

The method of claim 26, wherein the coordinates of the bounding box are associated with corners of the bounding box.

### Claim 28 (depends on 27)

The method of claim 27, wherein the probability distribution is a histogram associated with probabilities of one or more corners of the bounding box being placed at one or more coordinates within the image.

### Claim 29 (depends on 16)

The method of claim 16, wherein the target coordinates include x-y coordinates of a coordinate system associated with the image.