ARTIFICIAL INTELLIGENCE SYSTEMS AND METHODS FOR INTERIOR FURNISHING

1 . A system for generating a furnishing plan for a property, comprising:

a communication interface configured to receive a floor plan of the property, at least one candidate furnishing item to be placed in the property and attributes of the candidate furnishing item, and a first neural network model; and

at least one processor configured to:

generate a mask based on the floor plan of the property and the attributes of the candidate furnishing item;

generate proposals for placing the candidate furnishing item in the property based on the mask and the attributes of the candidate furnishing item;

determine a probability for each proposal using the first neural network model, wherein the first neural network model is trained with a position query algorithm;

select one or more proposals having the highest probabilities; and

generate the furnishing plan for the property based on the one or more proposals.

2 . The system of claim 1 , wherein the first neural network model is a Graph Neural Network (GNN).

3 . The system of claim 1 , wherein the communication interface is further configured to receive attributes of at least one existing furnishing item in the property, wherein the at least one processor is configured to generate the mask additionally based on the attributes of the existing furnishing item.

4 . The system of claim 1 , wherein the mask comprises a plurality of pixels each having either a first value or a secondary value, the first value indicating the corresponding pixel is not available for placing the candidate furnishing item and the second value indicating the corresponding pixel is available for placing the candidate furnishing item.

5 . The system of claim 4 , wherein to generate proposals for placing the candidate furnishing item in the property based on the mask and the attributes of the candidate furnishing item, the at least one processor is further configured to:

select discrete pixels in the mask that each have the second value; and

generate the proposals for placing the candidate furnishing item at the selected discrete pixels.

6 . The system of claim 1 , wherein the at least one processor is further configured to:

rank the proposals based on the respective probabilities; and

select the one or more proposals with the highest probabilities according to the ranking, wherein the one or more proposals include the proposal with the highest probability and one or more proposals having probabilities differing from the highest probability with less a threshold difference.

7 . The system of claim 1 , wherein for each selected proposal, the at least one processor is further configured to:

generate a new mask based on the floor plan of the property, attributes of the candidate furnishing item placed according to the selected proposal, and the attributes of a new candidate furnishing item;

generate proposals for placing the new candidate furnishing item in the property based on the new mask and the attributes of the new candidate furnishing item;

determine a probability for each proposal using the first neural network model; and

select one or more proposals having the highest probabilities.

8 . The system of claim 1 , wherein the communication interface is further configured to receive a second neural network model, wherein the at least one processor is further configured to:

calculate a rationality score for the furnishing plan based on the second neural network model, wherein the second neural network model is trained with a position query algorithm.

9 . The system of claim 8 , wherein to calculate the rationality score for the furnishing plan, the at least one processor is further configured to:

generate a graphical for the furnishing plan;

for each furnishing item placed at a position according to the furnishing plan, predict a probability that the furnishing item would have been recommended to be placed at the position using the second neural network model; and

calculate the rationality score based on the probabilities of the furnishing items in the furnishing plan.

10 . The system of claim 9 , wherein the at least one processor is further configured to:

calculate a binary score for the furnishing plan based on one or more predetermined furnishing rules;

calculate a distribution score based on a distribution of furnishing items according to the furnishing plan; and

calculate a functionality score indicating a functionality of the furnishing plan.

11 . The system of claim 10 , wherein to calculate the distribution score, the at least one processor is further configured to:

generate a furnished mask for the furnishing plan;

identify placement pixels in the furnished mask corresponding to where the furnishing items are placed according to the furnishing plan; and

determine the distribution score based on a distribution of the placement pixels.

12 . The system of claim 10 , wherein to calculate the functionality score, the at least one processor is further configured to:

for each pair of furnishing items in the furnishing plan, determine a moving distance between the furnishing items in the pair; and

determine the functionality score based on the moving distances weighted by predetermined weights.

13 . The system of claim 10 , the at least one processor is further configured to calculate an evaluation score for the furnishing plan, wherein the evaluation score is a weighted sum of the rationality score, the distribution score, and the functionality score, which is further weighted by the binary score.

14 . A computer-implemented method for generating a furnishing plan for a property, comprising:

receiving, by communication interface, a floor plan of the property, at least one candidate furnishing item to be placed in the property, and attributes of the candidate furnishing item, and a first neural network model;

generating, by at least one processor, a mask based on the floor plan of the property and the attributes of the candidate furnishing item;

generating proposals for placing the candidate furnishing item in the property based on the mask and the attributes of the candidate furnishing item;

determining, by the at least one processor, a probability for each proposal using the first neural network model, wherein the first neural network model is trained with a position query algorithm;

selecting one or more proposals having the highest probabilities; and

generating, by the at least one processor, the furnishing plan for the property based on the one or more proposals.

15 . The method of claim 14 , wherein the mask comprises a plurality of pixels each having either a first value or a secondary value, the first value indicating the corresponding pixel is not available for placing the candidate furnishing item and the second value indicating the corresponding pixel is available for placing the candidate furnishing item,

wherein generating proposals for placing the candidate furnishing item in the property based on the mask and the attributes of the candidate furnishing item further comprises:

selecting discrete pixels in the mask that each have the second value; and

generating the proposals for placing the candidate furnishing item at the selected discrete pixels.

16 . The method of claim 14 , further comprising, for each selected proposal:

generating a new mask based on the floor plan of the property, attributes of the candidate furnishing item placed according to the selected proposal, and the attributes of a new candidate furnishing item;

generating proposals for placing the new candidate furnishing item in the property based on the new mask and the attributes of the new candidate furnishing item;

determining a probability for each proposal using the first neural network model; and

selecting one or more proposals having the highest probabilities.

17 . The method of claim 14 , further comprising:

receiving a second neural network model, wherein the second neural network model is trained with a position query algorithm;

generating a graphical for the furnishing plan;

for each furnishing item placed at a position according to the furnishing plan, predicting a probability that the furnishing item would have been recommended to be placed at the position using the second neural network model; and

calculating a rationality score for the furnishing plan based on the probabilities of the furnishing items.

18 . The method of claim 17 , further comprising:

calculating a binary score for the furnishing plan based on a predetermined furnishing rules;

calculating a distribution score based on a distribution of furnishing items according to the furnishing plan; or

calculating a functionality score indicating resident mobility of the furnishing plan.

19 . The method of claim 18 , further comprising calculating an evaluation score for the furnishing plan, wherein the evaluation score is a weighted sum of the rationality score, the distribution score, and the functionality score, which is further weighted by the binary score.

20 . A non-transitory computer-readable medium having stored thereon computer instructions, when executed by at least one processor, perform a method for generating a furnishing plan for a property, comprising:

receiving a floor plan of the property, at least one candidate furnishing item to be placed in the property, and attributes of the candidate furnishing item, and a first neural network model;

generating a mask based on the floor plan of the property and the attributes of the candidate furnishing item;

generating proposals for placing the candidate furnishing item in the property based on the mask and the attributes of the candidate furnishing item;

determining a probability for each proposal using the first neural network model, wherein the first neural network model is trained with a position query algorithm;

selecting one or more proposals having the highest probabilities; and

generating the furnishing plan for the property based on the one or more proposals.