# US 2025/0061153 A1 · Iterative spatial graph generation

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2025/0061153 A1
- Document ID: US20250061153A1
- Status: Published application
- Kind: A1
- Application number: 18/935,222
- Filed: 2024-11-01
- Published: 2025-02-20
- Earliest claimed: 2019-03-22
- Assignees: NVIDIA Corporation
- Applicants: Nvidia Corporation
- Inventors: Hang Chu; Daiqing Li; David Jesus Acuna Marrero; Amlan Kar; Maria Shugrina; Ming-Yu Liu; Antonio Torralba Barriuso; Sanja Fidler
- Web page: https://patentgrep.com/patent/US20250061153A1

## CPC

- G06F 16/9024: {Graphs; Linked lists (G06F16/9027 takes precedence)}
- G06F 18/29: {Graphical models, e.g. Bayesian networks}
- G06F 30/13: Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
- G06F 30/27: using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
- G06N 3/045: Combinations of networks
- G06N 3/0455: Auto-encoder networks; Encoder-decoder networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/047: Probabilistic or stochastic networks
- G06N 3/0475: Generative networks
- G06N 3/08: Learning methods
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N 3/09: Supervised learning
- G06N 5/04: Inference or reasoning models
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G06N 20/20: Ensemble learning
- G06V 10/764: using classification, e.g. of video objects
- G06V 10/82: using neural networks
- G06V 10/84: using probabilistic graphical models from image or video features, e.g. Markov models or Bayesian networks
- G06V 20/182: {Network patterns, e.g. roads or rivers}
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

A generative model can be used for generation of spatial layouts and graphs. Such a model can progressively grow these layouts and graphs based on local statistics, where nodes can represent spatial control points of the layout, and edges can represent segments or paths between nodes, such as may correspond to road segments. A generative model can utilize an encoder-decoder architecture where the encoder is a recurrent neural network (RNN) that encodes local incoming paths into a node and the decoder is another RNN that generates outgoing nodes and edges connecting an existing node to the newly generated nodes. Generation is done iteratively, and can finish once all nodes are visited or another end condition is satisfied. Such a model can generate layouts by additionally conditioning on a set of attributes, giving control to a user in generating the layout.

## Claims

### Claim 1

A system comprising:
at least one processor; and
memory including instructions that, when executed by the at least one processor, cause the system to:
receive a spatial layout including a plurality of spatial control points spaced along individual roadways of a geographic region;
receive an indication of a layout style to be used to expand the spatial layout;
encode, for individual spatial control points of the spatial layout, information indicating one or more incoming roadway segments;
infer, based in part upon the encoded information for the one or more incoming roadway segments and according to the layout style, one or more outgoing roadway segments to one or more newly-added spatial control points;
determine, using one or more selection criteria, whether to add the one or more outgoing roadway segments and the one or more newly-added spatial control points to the spatial layout; and
provide the expanded spatial layout as representative of a simulated geographic region having the indicated layout style.

### Claim 2 (depends on 1)

The system of claim 1, wherein the instructions when executed further cause the system to:
encode the information using an encoder recurrent neural network (RNN) for encoding the one or more incoming roadway segments.

### Claim 3 (depends on 1)

The system of claim 1, wherein the instructions when executed further cause the system to:
infer the one or more outgoing roadway segments and the one or more newly-added spatial control points using a decoder recurrent neural network (RNN).

### Claim 4 (depends on 1)

The system of claim 1, wherein the instructions when executed further cause the system to:
remove at least one of the outgoing roadway segments and the newly-added spatial control points from consideration for addition to the spatial layout.

### Claim 5 (depends on 1)

The system of claim 1, wherein the one or more incoming roadway segments are within a specified proximity of the individual spatial control points.

### Claim 6 (depends on 1)

The system of claim 1, wherein the selection criteria includes the one or more outgoing roadway segments being less than a threshold distance.

### Claim 7 (depends on 1)

The system of claim 1, wherein the instructions when executed further cause the system to:
simulate autonomous navigation using the expanded spatial layout.

### Claim 8

A computer-implemented method, comprising:
receiving a spatial layout including a plurality of spatial control points spaced along individual roadways of a geographic region;
receiving an indication of a layout style to be used to expand the spatial layout;
encoding, for individual spatial control points of the spatial layout, information indicating one or more incoming roadway segments;
inferring, based in part upon the encoded information for the one or more incoming roadway segments and according to the layout style, one or more outgoing roadway segments to one or more newly-added spatial control points;
determining, using one or more selection criteria, whether to add the one or more outgoing roadway segments and the one or more newly-added spatial control points to the spatial layout; and
providing the expanded spatial layout as representative of a simulated geographic region having the indicated layout style.

### Claim 9 (depends on 8)

The computer-implemented method of claim 8, further comprising:
storing the plurality of spatial control points to a processing queue to be pulled from to expand the spatial layout.

### Claim 10 (depends on 8)

The computer-implemented method of claim 8, further comprising:
removing at least one of the outgoing roadway segments and the newly-added spatial control points from consideration for addition to the spatial layout.

### Claim 11 (depends on 8)

The computer-implemented method of claim 8, wherein the one or more incoming roadway segments are within a specified proximity of the individual spatial control points.

### Claim 12 (depends on 8)

The computer-implemented method of claim 8, wherein the selection criteria includes the one or more outgoing roadway segments being less than a threshold distance.

### Claim 13 (depends on 8)

The computer-implemented method of claim 8, further comprising:
simulating autonomous navigation using the expanded spatial layout.

### Claim 14 (depends on 8)

The computer-implemented method of claim 8, further comprising:
receiving the layout style through at least one style input or analyzing one or more geographic roadways.

### Claim 15 (depends on 8)

The computer-implemented method of claim 8, wherein at least one of the individual spatial control points correspond to an intersection and wherein a corresponding outgoing roadway segment is a road segment leading from the intersection.

### Claim 16

A path layout generator, comprising:
an interface for receiving data indicative of at least one layout style;
at least one processor; and
memory including instructions that, when executed by the at least one processor, cause the path layout generator to:
determine a plurality of pending spatial control points, spaced along paths of a geographic region, that have not yet been analyzed by the path layout generator for a path layout;
encode, for individual pending spatial control points, incoming information indicating one or more incoming path segments;
decode, using the encoded incoming information and according to the at least one layout style, outgoing information indicating one or more new spatial control points and corresponding outgoing path segments to the one or more new spatial control points; and
expand the path layout with the one or more new spatial control points and the corresponding outgoing path segments as representative of a simulated geographic region having the at least one layout style.

### Claim 17 (depends on 16)

The road layout generator of claim 16, wherein the instructions when executed further cause the system to:
encode the incoming information or decode the outgoing information using a vector graphics-based generative neural network.

### Claim 18 (depends on 17)

The road layout generator of claim 17, wherein the instructions when executed further cause the system to:
iteratively expand path layout with subsequent new spatial control points and subsequent corresponding outgoing path segments for additional pending spatial control points that have not yet been analyzed.

### Claim 19 (depends on 16)

The road layout generator of claim 16, wherein the data indicative of the layout style includes at least one of an aerial image of a location, a map, a sketch, a style specification, or an initial layout region.

### Claim 20 (depends on 16)

The road layout generator of claim 16, wherein the instructions when executed further cause the system to:
enable a user to provide layout information through an interactive interface, the one or more new spatial control points being decoded further based on the provided layout information.